A Computational Cognitive Grammar of English
Last Updated 3 September 2015
with Mary Freiman, Stu Rodgers and Alan Ball
Table of Contents:
Chapter 1: Introduction
Double-R Grammar is a computational cognitive grammar of English that details
a system of grammatical representation focused on capturing
two key dimensions of meaning
and relational meaning.
Double-R identifies the referring expressions in the input (e.g. object referring expression or nominal,
situation referring expression or clause)
and the relationships between these referring expressions (e.g. transitive verb relating
a subject and an object).
Double-R representations are linguistic,
or grammatical in the traditional sense, but not purely syntactic
— there is no autonomous syntax component.
Grammatical representations are assumed to be semantically motivated, subject to the challenge of
encoding multiple dimensions of meaning in a linear code.
Double-R includes a cognitively motivated
language analysis mechanism that adheres to
two well-established constraints on Human Language Processing (HLP)
— incremental and interactive processing.
Double-R incrementally analyzes the written linguistic input one word or multi-word unit at a time,
using all available grammatical (and eventually semantic) information interactively (in parallel) to make the best choice at each choice point.
Lexical items in the input project constructions which set up expectations that drive processing.
Once a choice is made, it is assumed to be correct, and Double-R proceeds incrementally forward.
However, the subsequent input may require modification of the evolving representation
via a non-monotonic mechanism of context accommodation.
Overall, the processing mechanism is pseudo-deterministic in that it pursues the single best analysis
given the current input and context, but accommodates the subsequent input and context when necessary.
Double-R is implemented as a large-scale cognitive model
in the ACT-R cognitive architecture
and is approaching the grammatical breadth of leading computational linguistic systems,
without being tuned to a specific corpus or being limited to purely syntactic analysis.
Although most of the examples in this document show the representation and processing of isolated sentences, Double-R accepts input
from single words up to an entire document of text.
Current capabilities for resolving cross-sentential dependencies (e.g. anaphora) are limited, as are discourse capabilities more generally.
During the processing of an input text,
Double-R creates a collection of nested ACT-R chunks
(i.e. frame-like representations consisting of a collection of slot-value pairs where the value of a slot may be a chunk).
At the end of processing, these ACT-R chunks are converted into tree diagrams for visualization.
The diagram creation capability consists of Lisp code that generates bracketed structures from the ACT-R chunks and phpSyntaxTree which generates diagrams from the bracketed structures.
PhpSyntaxTree is a product of Mei and André Eisenbach.
The bracketing code was developed by Andrea Heiberg.
In linearizing the slot values of the nested ACT-R chunks, the bracketing code provides the rough equivalent of a language generation capability.
Andrea worked with Jack Harris to interface the bracketing code to phpSyntaxTree.
The tree diagrams are customizable to some extent.
Grammatical features may or may not be displayed, and when they are displayed they are only displayed at the level of the clause or nominal.
Some elements of the underlying chunk representation are not displayed in the diagrams
— especially slots lacking a value.
We refer to Double-R representations as grammatical or linguistic (but not syntactic) representations.
By this we mean that they include information about the grammatical function as well as the form of the linguistic elements in the input.
We assume that grammatical functions like subject, object, specifier, head,
and grammatical features like animacy, gender and number are semantically motivated.
We also assume, as in Langacker's Cognitive Grammar and traditional grammar, that parts of speech like noun,
verb and adjective are semantically motivated.
We use the term grammatical as it is traditionally used, to reflect these assumptions.
These grammatical representations map into non-linguistic representations of the situations and objects
that they describe within the context of a situation model. These non-linguistic representations are in the spirit of
Jackendoff's Conceptual Semantics and are under development by Stu Rodgers.
The grammatical representations encode two key dimensions of meaning:
referential and relational
— hence the name Double-R.
Double-R identifies the referring expressions in the input
(e.g. object referring expression or nominal, situation referring expression or clause)
and the relationships between these referring expressions.
The key relational elements include verbs, adjectives, adverbs,
prepositions and conjunctions
(but not nouns).
The processing of relational elements leads to projection of constructions which predict the occurrence
of the elements they relate.
Double-R is most closely aligned with Langacker's Cognitive Grammar and
collectively Construction Grammar
(most recently Sag's shift from Head-Driven Phrase Structure Grammar (HPSG) to
Sign Based Construction Grammar (SBCG) and
Jackendoff's shift in this direction as well).
Double-R can best be viewed as a formalization and computational implementation of ideas from
Cognitive and Construction Grammar.
From the perspective of English grammar, Double-R aligns with Huddleston and Pullum's The Cambridge Grammar of the English Language and
to lesser extent Quirk, Greenbaum, Leech and Svartvik's A Comprehensive Grammar of the English Language,
and Biber, Johansson, Leech, Conrad & Finegan's Longman Grammar of Spoken and Written English.
From the perspective of formal linguistics, Double-R aligns with the Simpler Syntax of Culicover and Jackendoff.
Double-R adheres to well-established cognitive constraints on Human Language Processing (HLP), including the incremental and interactive nature of HLP.
Double-R processes the input incrementally, one word or multi-word unit at a time, using a perceptual span of 12 characters to perceive the current input.
No preprocessing of the input is required.
There is no separate tokenizing, part of speech tagging or syntactic parsing.
All processing occurs interactively within Double-R.
The word recognition subcomponent, which is fully integrated with the rest of the system, is being developed by Mary Freiman.
The contents of the perceptual span activate words and multi-word units in the Mental Lexicon component of declarative memory.
Activation spreads from the letters, trigrams and space delimited units in the perceptual span.
The most highly activated declarative memory element, which need not be an exact match, is retrieved from memory and used in subsequent processing.
The current computational implementation comprises ~1400 productions and ~58,000 lexical items, and is capable of processing a broad range of
English language constructions.
The mental lexicon increased from ~8000 to ~58,000 lexical items in 2011. This expansion
was made possible by use of the Corpus of Contemporary American English (COCA) which provides
information about the frequency of occurrence of words in their various parts of speech.
We are working on stabilizing the behavior of Double-R with this comprehensive lexicon
and on extending the grammatical coverage to encompass the expanded lexicon.
On a 64-bit quad core computer with 8 Gig RAM, Double-R is capable of processing ~150 words per minute (wpm) with the 58,000 word mental lexicon.
Double-R also processes ~140 wpm in ACT-R cognitive processing time.
By comparison, fluent adult reading rates are in the range of 200-300 wpm.
The key to achieving adult reading rates is the ability to process multi-word units, including units that are larger than a single perceptual span.
Such units not only speed up processing, but they are less ambiguous than individual words and facilitate determination of meaning.
We are working on extending the number of multi-word units in the mental lexicon and adding the capability to recognize units larger than a single perceptual span.
This view of multi-word units as crucial for rapid comprehension
contrasts with that of Sag et al. (2002) where they are viewed as A Pain in the Neck for NLP.
Double-R is a work in progress. It currently has broad enough coverage of English to be used in the
development of a synthetic teammate capable of communicating via text chat with two human
teammates in a Unmanned Aerial Vehicle (UAV) reconnaissance mission simulation.
Although we are making incremental progress in expanding Double-R's grammatical coverage,
it does not currently have the grammatical coverage of
leading computational linguistic systems
which use automated machine learning techniques operating over annotated corpora like the
Penn Treebank corpus in order to bootstrap grammar development. Much of the current capability of Double-R
has been manually encoded, although we also use automated techniques where practical (e.g. in creation
of the mental lexicon).
The downside is that this is a slower development process than using fully automated techniques.
The upside is that we have full control and
can incrementally improve the behavior of Double-R in ways that are not available to approaches
which rely exclusively on automated techniques. For example, it is straightforward in Double-R
to add a new grammatical category to facilitate the processing of some previously unhandled
construction. This is not possible in systems which rely on use of an annotated corpus
with a fixed set of grammatical categories
— at least not without first updating the annotated corpus to reflect the new category.
Although updating an annotated corpus is possible,
the updated corpus is no longer compatible with the original. Given the way that annotated
corpora like the Penn Treebank are used to evaluate competing systems, changing the annotated corpus
is not typically an option. This inability to update the corpus
has the negative effect of stifling innovation in representations
— at least incremental innovation of a particular annotated corpus
— even when such
innovations might improve processing. For example, it is not possible to train grammars on
proper prepositional phrase modifier attachment in nominals, since modifiers are simply listed at a fixed level
in the Penn Treebank. If the Penn Treebank were updated to support this, the updated corpus
would be incompatible with the original corpus. It would be necessary to get researchers who
use the Penn Treebank for comparison with competing approaches to adopt the update en masse.
No one researcher or institution has the
influence to make this happen.
Since Double-R does not rely on Penn Treebank annotations
— although the Penn Treebank was used to determine the subcategorization frames of
verbs (e.g. intransitive, transitive, ditransitive)
— changing the grammatical categories
is not a problem. What is a problem is comparing the performance of Double-R to Penn Treebank
based parsers. It is simply not possible to use established techniques to do this.
Despite this difficulty, given the current capabilities and the ability to incrementally improve Double-R,
we expect Double-R to eventually match or exceed the state-of-the-art performance
of leading computational linguistic systems.
To test the performance
of Double-R against leading computational linguistic systems, we use the
parser as an exemplar since it is available on line.
However, since Double-R generates representations which are
not fully compatible with MSR Splat (or other systems based on the Penn Treebank), it is difficult
to do automated comparisons over large corpora (tyically a test set from the Penn Treebank)
as is the current standard for comparison.
For this reason, we rely on sampling techniques to do the comparisons.
We take a random sample from a given corpus, run the sample thru Double-R and MSR Splat, and
manually evaluate the results. Note that this approach avoids the need for a gold standard
like the Penn Treebank to do the evaluation automatically.
We recently evaluated a random sample taken from the text chat corpus
we are using in the development of the synthetic teammate. Although this corpus is guiding
development of Double-R, it is not annotated and Double-R is not specifically trained to
handle just this corpus, although the mental lexicon does include the proper nouns from this corpus.
Based on our analysis of a random sample of 51 text chat messages, Double-R
correctly processed 24 messsages (about 50% correct) and MSR Splat correctly processed 16.
The details of this analysis are provided in Appendix E.
The reason the success rates are so low
is largely due to the nature of the text chat corpus combined with a definition of correctness
which applies to the entire message.
Although this document if primarily focused on describing the capabilities of Double-R, it is also
intended as a resource for identifying gaps in coverage and areas in need of improvement. We will not
hesitate to point out such gaps. As those gaps are filled and improvements made, this document will
Chapter 2: Methodological Commitments
A key commitment of our language comprehension
research is development of a computational model which is at once
cognitively plausible and functional. We believe that
adherence to well-established cognitive constraints will
facilitate the development of functional systems by pushing
development in directions that are more likely to be
successful. There are short-term costs associated with
adherence to cognitive constraints; however, we have
already realized longer-term benefits. For example, the
integration of a word recognition capability with ACT-R's
perceptual system and higher-level linguistic processing has
facilitated the recognition and processing of multi-word
expressions and multi-unit words in ways that are not
available to systems with separate word tokenizing and part
of speech tagging processes. Using an available tokenizer
and part of speech tagger would have initially facilitated
development, but the cognitive implausibility of using
staged tokenizing and part of speech tagging led us to reject
this approach. The benefits that we have realized as a result
of this decision are described below and elsewhere in this document.
As another example, we rely on use of a mental lexicon of word knowledge
based on our understanding of the human mental lexicon. During
language analysis, words in the input activate corresponding
words in the mental lexicon. The most highly activated word is retrieved
and guides subsequent processing. Words in the mental lexicon
encode knowledge about their form (e.g. letters and trigrams),
their part of speech (e.g. noun, verb), their grammatical features
(e.g. number, animacy, gender; tense, aspect, voice), their grammatical function
(e.g. head of object referring expression, specifier of clause),
and, to some extent, their meaning (e.g. semantic type, referential type).
In addition, the mental lexicon encodes knowledge about the frequency
of use of words in their various parts of speech.
In Double-R, the mental lexicon is a much richer resource of word knowledge
than are the simple word/part of speech lists that are typically used in computational linguistic
systems. Creation of the mental lexicon relied on use of a combination
of automated and manual techniques. Overall, it was a resource intensive effort,
and we continue to make improvements. As the mental lexicon has expanded and
improved, so have the language analysis capabilities of Double-R.
There is extensive psycholinguistic evidence that human
language processing is incremental and interactive (Gibson
& Pearlmutter, 1998; Altmann, 1998; Tanenhaus et al.,
1995; Altmann & Steedman, 1988). Garden-path effects,
although infrequent, strongly suggest that processing is
essentially serial at the level of phrasal and clausal analysis
(Bever, 1970). Consider
These sentences all lead the reader down a garden-path that results in a disruption of normal incremental processing
at the highlighted word or the end of the sentence. This disruption should not occur if alternative representations were
maintained in parallel, suggesting that human language processing (HLP) is essentially serial and incremental at the phrase and clause level.
Humans appear to pursue a
single analysis which is only occasionally disrupted,
requiring reanalysis. One of the great challenges of
psycholinguistic research is to explain how humans can
process language effortlessly and accurately given the
complexity and ambiguity that is attested (Crocker, 2005).
As Boden (2006, p. 407) notes,
- The horse raced past the barn fell (raced past the barn is a reduced relative clause, not a main clause)
- The old dog the young (dog is a verb, not a noun)
- John gave her books (her is the indirect object and books is the object, her books is not the object)
- The boy said the pledge of allegiance was too long (the pledge of allegiance was too long is a complement clause,
the pledge of allegiance is not the object)
would explain the introspective ease and speed of speech
However, given the rampant ambiguity of
natural language, a deterministic mechanism would need
access to the entire input before making a decision. Marcus
(1980) proposed a deterministic parser with a limited
lookahead capability to capture the trade-off between the
efficiency of human parsing and the limitations with respect
to garden-path inputs. However, there is considerable
evidence that HLP is inconsistent with extensive lookahead,
delay or underspecification
— the primary serial mechanisms
for dealing with ambiguity without backtracking or
reanalysis. Instead of lookahead, the HLP
engages in thinkahead, biasing and predicting what will
come next, rather than waiting until the next input is
available before deciding on the current input.
Summarizing the psycholinguistic evidence, Altmann &
Mirkovic (2009, p. 605) claim
The view we are left with is
a comprehension system that is 'maximally incremental'; it
develops the fullest interpretation of a sentence fragment at
each moment of the fragment's unfolding.
Although phrase and clause level processing is serial and incremental, lower level processes of word recognition
suggest parallel, activation-based processing mechanisms
(McClelland & Rumelhart, 1981; Paap et al., 1982).
Determining the part of speech of a word is strongly contrained by the preceding context. Consider the word bank:
In the context of the, bank-the-noun is strongly preferred.
In the context of to, bank-the-verb
is strongly preferred. Retrieval of the appropriate part of speech of bank is highly context dependent and interactive.
These cognitive constraints legislate against staged analysis models
— the current standard in computational linguistics. All levels of analysis must at least be
highly pipelined together (i.e. word by word, or morpheme by morpheme), if not, in addition, allowing
feedback from higher to lower levels. They also suggest the
need for hybrid systems which incorporate a mixture of
parallel and serial mechanisms, with lower levels of
processing being primarily parallel, probabilistic and
interactive, while higher levels of analysis are primarily
serial, deterministic and incremental.
Functional Language Analysis
Double-R Grammar is an attempt to build a broadly functional model
of language analysis — and ultimately language comprehension —
that is also cognitively plausible. In attempting to be
cognitively plausible, we adhere to well
established cognitive constraints on human language
processing (HLP) and do not adopt any computational
techniques which are obviously not cognitively plausible.
For example, we attempt to model the real-time
language processing behavior of humans using a pseudo-deterministic,
serial processing mechanism operating over
a parallel, probabilistic, activation substrate.
The parallel, probabilistic substrate activates constructions
corresponding to the linguistic input — constrained by the
current context — and the serial processing mechanism
selects from among the activated constructions and
integrates them into a coherent representation. Overall, the
processing mechanism is highly interactive and
incremental, allowing whatever grammatical or semantic
information is most relevant to be brought to bear in
making a decision that will usually be correct at each
choice point. The language analysis model does not
make use of computational techniques like a first pass
part-of-speech tagger that operates independently of a second
pass parser. Being non-incremental, such an approach is
not cognitively plausible.
It might be assumed that the commitments to building a functional
and cognitively plausible model are incompatible.
However, it is a basic claim of this research that a non-functional
model cannot be cognitively plausible. That is, a cognitive model
that doesn't actually perform the cognitive task it purports to model
is at best suspect,
even if the model fits some empirical measure like reaction time.
As an example, the EZReader model (Reichle, Raynor & Pollatsek, 2003) —
a model of lexical access during reading —
is not a cognitively plausible model of reading since it doesn't
perform the reading task. Although EZReader
is capable of modeling a wide range of empirical phenomena related to
lexical access, since it doesn't actually complete the reading task,
it is clearly not a model of reading — the developers of EZReader
are quick to acknowledge this point (despite the model's name). Further, any claims about lexical access
must be taken with a grain of salt, since lexical access is an
important subcomponent of reading which cannot easily be studied in isolation
from the overall reading task. As a concrete example of where the attempt
to study the lexical access subcomponent in isolation runs astray, the developers of
EZReader claim that at the processing of each word, it takes only 25 msec
on average for all higher level processing associated with reading
to influence the next eye movement.
In ACT-R terms, there is insufficient time for a single higher-level production to fire
and influence the programming of the next eye movement. Such a claim is
obviously false. It arises because the EZReader model makes no attempt to
actually read the linguistic input.
The use of the term functional to describe a model like
Double-R Grammar can be further elaborated. Ultimately, a functional
model of reading should do just that — read and understand the linguistic input.
In this respect, Double-R Grammar currently falls short since it is not
currently capable of full language comprehension. Rather, Double-R Grammar
is a model of language analysis which creates linguistic representations
that encode some aspects of meaning — especially referential and
relational meaning. While these two dimensions of meaning
are important, they are not the whole story. Until Double-R Grammar
is capable of full language comprehension, claims of cognitive plausibility
can be criticized.
There are two directions in which Double-R Grammar must be advanced
in order demonstrate full language comprehension:
Research is ongoing in both these directions.
- Deeper and more complete semantic and pragmatic analysis
- Broader coverage
With respect to deeper semantic analysis, efforts to map Double-R representations
into a Situation Model are underway. At present,
these efforts are restricted to the domain of an Unmanned Aerial Vehical (UAV)
reconnaissance task. The Situation Model represents the objects and situations
that are relevant to this domain and provides the referential grounding for
the referring expressions in Double-R's linguistic representations.
Efforts are also underway to support
Discourse Modeling within this domain.
Other aspects of pragmatic analysis are yet to be implemented.
These capabilities provide the deeper understanding that is needed
for development of a synthetic teammate
that is capable of performing the piloting task within the UAV reconnaissance domain,
and get us closer to the ultimate objective of a model that is capable of full
language understanding (at least within the UAV reconnaissance domain).
With respect to breadth of coverage,
a model that is limited to some specialized collection of inputs
designed to test some isolated psycholinguistic behavior is empirically lacking.
For example, a system which models garden-path
phenomena, but can't model common-or-garden sentences,
is not considered functional. In addition, the term
functional applies to the addition of mechanisms, as
needed, to model a broad range of inputs. For example, the
modeling of wh-questions requires the addition of
mechanisms to support the fronting of a wh-expression and
the binding of this fronted expression with the trace of an
implicit argument or adjunct (or alternative mechanisms
for indicating this relationship). Likewise, the modeling of
yes-no questions requires mechanisms to support the
inversion of the subject with the first auxiliary (relative to
declarative sentences). The overall functional goal is to be
able to handle the basic grammatical patterns of English
such that the model can be used in a real world application like the UAV reconnaissance task.
Although the language analysis capabilities of Double-R Grammar are quite broad,
the domain specificity of the Situation Model still limits overall
functionality and limits claims of cognitive plausibility.
There is some acknowledgement within the cognitive
modeling community that we need to be building larger-scale
models with broader cognitive capabilities. This
acknowledgement is reflected in the frequent reference to
Newell's 20 questions critique (Newell, 1973) of
cognitive science research (cf. Anderson & Lebiere, 2003;
Anderson, 2007; Byrne, 2007). Anderson & Lebiere (2003)
argue that the ACT-R cognitive architecture answers
Newell's 20 questions critique in assessing ACT-R's
capabilities with respect to the Newell Test (Newell, 1990)
for a theory of cognition. The Newell Test lists twelve
functional criteria considered essential for a human
cognitive architecture. Although ACT-R does not
completely satisfy all twelve criteria, it does well enough
to merit serious consideration as a functional architecture.
On the other hand, although the ACT-R cognitive
architecture addresses Newell's 20 questions criticism,
cognitive models developed within ACT-R typically
address specific, limited, cognitive phenomena tied closely
to simple laboratory experiments. The typical study
involves the development of a cognitive model that
matches the human data from some laboratory experiment,
demonstrating that the ACT-R cognitive architecture
provides the needed cognitive mechanisms — when
combined with task specific knowledge — to model the
human data. In addition, Young's (2003) notion of
compliancy is satisfied if the model was developed without
excessively challenging the cognitive architecture. A few
studies attempt to model more complex phenomena (Gray
& Schoelles, 2003; Fu et al., 2006) and there is also some
hope that smaller scale models can be integrated into more
complex composite models (Gray, 2007). But cognitive
modelers are loath to distance themselves from matching
human experimental data and this commitment
methodologically differentiates cognitive modeling from
other types of computational modeling. (Cognitive
modelers might argue that this is what makes their efforts
scientific in the Popperian sense.) Further, within the ACT-R
community, matching human data typically means
matching data from reaction time studies, since ACT-R
was specifically developed to support this kind of
modeling (Anderson & Lebiere cite this as the best
functional feature of ACT-R). Note that it is the cognitive
models developed within ACT-R which actually provide
the empirical validation of the cognitive architecture, since
the cognitive architecture itself is not capable of modeling
human behavior (although some steps have been taken to
automate the learning of experimental tasks so that the
architecture can directly model human behavior without
the intervention of creating a cognitive model).
Despite the functionalist claims of Anderson & Lebiere
(2003), recent variants of the ACT-R cognitive architecture
are (in part) motivated on minimalist principles by which
the architecture is only extended if extensive empirical
evidence is provided to motivate the extension.
According to Anderson & Taatgen (2008), from a theoretical perspective,
it is important to keep the architecture tightly constrained.
Otherwise, the architecture underconstrains models developed in
the architeture, and that makes it difficult to falsify either the model
or the architecture.
As evidence of this minimalist stance,
some functional mechanisms available in earlier ACT-R variants like
the unbounded goal stack and multi-level activation spread
have been removed from the architecture. The unbounded
goal stack has not held up to minimalist arguments and is
inconsistent with empirical results which show decayed
memory for previous goals. While the removal of the
unbounded goal stack is supported by empirical evidence,
the replacement of the goal stack by a single chunk goal
buffer appears to be more a reflection of the minimalist
bent than it is empirically motivated. Until there is
sufficient empirical evidence that a multiple chunk goal
buffer is needed, the minimalist argument suggests it be
limited to a single chunk. Ockham's razor is hard at work.
Not only the goal buffer, but all buffers in ACT-R are
limited to a single chunk. Functionally, the language analysis model
appears to need more than single chunk buffers. To
overcome this limitation, the language analysis model uses
a combination of grammatical function specific buffers
and multiple buffers linked together to create bounded buffer stacks
(limited to 4 chunks, consistent with empirical evidence of
short-term working memory capacity, cf. Cowan, 2001).
The grammatical function specific buffers provide links to
the outermost element of a deeply nested linguistic structure
— supporting primacy effects, whereas the buffer stacks
provide access to the most recent linguistic elements
— supporting recency effects.
Likewise, the elimination of multi-level activation spread
is based on empirical evidence against priming from the
word bull to the word milk via the intermediate word
cow. However, limiting activation to the spread from
slots in chunks in buffers to slots in declarative memory
chunks, with no subsequent spread of activation from slots
in declarative memory to other declarative memory chunks
imposes a hard constraint on possible systems of
representation. For example, in a declarative memory in which a cow
chunk is not directly linked to a bull chunk, no activation
spread is possible. In small-scale systems, this is not a
problem, but in large-scale systems, the proliferation of
direct links required to support single-level activation is explosive.
Further, chunks must have separate links for all possible
forms of activation including semantic, syntactic,
morphologic, phonologic, orthographic, etc., resulting in a
proliferation of links within individual chunks and making
it difficult to spread activation across levels (e.g., letters
can activate syllables and words directly, but how can
syllables activated by letters, spread activation to words
without multiple level activation?).
In sum, there appear to be competing motivations
influencing the development of ACT-R. On the one hand is
the desire to satisfy the Newell Test of functionality; and,
on the other hand is the small-scale approach to science
adopted within Popperian cognitive psychology and
against which Newell's 20 questions critique is addressed.
In our view, the key to bridging the gap
between current small-scale cognitive modeling and the
development of large-scale functional systems is to adopt a
functionalist perspective at the level of cognitive models
(as well as cognitive architecture) — without giving up on
cognitive plausibility. Given the complexity of the
cognitive systems we are modeling, it may not be feasible
to pursue low-level empirical studies — at least not until we
have a working cognitive model built from the functional
perspective. Once a working cognitive model is available,
the functional mechanisms proposed in the development of
the model can be subjected to empirical validation and
Ockham's razor (i.e. can a model with fewer mechanisms
model the same complex behavior). From the functionalist
perspective, it is premature to enforce minimalist
assumptions in the absence of a functional model. Further,
empirical validation of small pieces of a complex system in
the absence of a working model are of limited value (as
suggested by Newell's 20 questions critique). Mechanisms
which are sufficient in a small-scale model of a single
cognitive phenomenon are unlikely to be sufficient in a
large-scale functional model of complex cognitive
behavior. Scaling up to complex cognitive phenomena
means adding additional mechanisms and integrating these
mechanisms in complex ways which cannot be predicted
on the basis of small-scale models. Ockham's razor may
well be counter-productive in such contexts. As Roelofs
(2005) notes, although Ockham's razor favors the simplest
model that covers a set of phenomena, it does not
simultaneously favor modeling the simplest set of
phenomena. Further, Tenenbaum (2007) argues that it is
important to consider the trade-off between simplicity and
fit (in the development of models of language acquisition).
The simplest model which covers a set of phenomenon is
unlikely to be the best fit to the data and the best fitting
model is unlikely to be the simplest. The preferred model
will necessarily trade-off simplicity and fit. In addition, as
the set of phenomena to be modeled is increased, a more
complex model will be required to provide the same degree
of fit. Further, increases in complexity are likely to be
exponential, rather than linear, with increases in the
number of objects in a model.
What is not being proposed is an approach to cognitive
modeling research which ignores well-established
cognitive constraints on human behavior. While such an
approach is acceptable in some Artificial Intelligence
circles where the goal is to develop intelligent systems
using advance computational techniques, regardless of the
cognitive plausibility of those techniques, the approach
being proposed here accepts the validity of cognitive
constraints and integrates them into the development of
complex cognitive mechanisms which are at once
functional and cognitively plausible. What is proposed is a
shift in methodology in which the conduct of small-scale
empirical studies is delayed or marginalized until a
working model of a complex task has been developed on
functionalist principles. Once a functional model is in
place, small-scale empirical validation of specific
components of the model and the application of minimalist
principles like Ockham's razor become relevant and
important. Until a functional model is in place, model
development is guided by cognitive constraints and
empirical validation at a gross level, without being
constrained to match specific data sets which would likely
derail, rather than facilitate, progress. A small-scale model
tuned to a specific data set is unlikely to generalize to meet
the larger functional requirements of a complex system.
Natural Language Processing → Human Language Processing
Marr (1982, 1992) put forward a strongly functionalist
approach to modeling biological information processing
problems in arguing that we should first identify the
computational mechanisms and constraints that are needed
to compute the complex phenomena being studied
(computational and algorithmic levels), before worrying
about how these mechanisms might be implemented in the
brain or other hardware (implementation level). As Boden
(1992) notes in describing Marr's position (Marr, 1992),
…a science of intelligence requires either 'Type-1'
models based on theoretical understanding of fundamental
(axiomatic) task-constraints or 'Type-2' implementations
of intelligent performance effected by 'the simultaneous
action of a considerable number of processes, whose
interaction is its own simplest description'.
Although Marr prefers an approach to research which focuses on the
development of Type-1 theories which are explicit and
computational, he acknowledges that this is not always
possible, and often Type-2 theories are the best
explication of a complex information processing problem
that can be developed. Marr places human language
processing in this latter category suggesting that a
Type-1 theory corresponding to Chomsky's notion of
competence may not be possible, with only Type-2
theories which consider the process of mapping a multidimensional
(mental) representation in the head of the
speaker into a
one-dimensional form for transmission as a
sequential utterance…to be retranslated back into a rough
copy of the original in the head of the listener (Marr,
1992, p. 138)
Left unstated in Marr (1992) is the methodology by which
models of complex cognitive systems are empirically
validated. Within AI, it is often assumed that the primary
empirical goal is to model input-output behavior. However,
as argued in Ball (2006), we do not believe it is possible to
model the input-output behavior of complex cognitive
systems like language without serious consideration and
computational implementation of the internals of language
processing in humans. If we are to delve inside the black box
of cognition, then we need a methodology for
empirically validating the representations and mechanisms
proposed for inclusion in the black box. However, as
argued above, small-scale Popperian falsification of
isolated hypotheses is likely to derail progress in the
development of functional systems. For those of us who
are interested in building large-scale models, such an
approach is not viable (although we are happy to consider
the small-scale experimental results of others researchers).
Instead, we should focus on identifying empirical
phenomena which can be validated at a gross level which
helps to focus development in promising directions without
side-tracking that development.
One good example of matching a cognitive constraint at a
gross level within NLP, is the requirement to be able to
process language incrementally in real-time. At Marr's
algorithmic level where parallel and serial processing
mechanisms are relevant, a language processing system
should be capable of incremental, real-time language
processing. For language processing, this means that the
performance of the system cannot deteriorate significantly
with the length of the input — as is demonstrably not the
case in humans. The simplest means of achieving this is in
a deterministic system (cf. Marcus, 1980). To the extent
that the system is not deterministic, parallelism (or some
other nonmonotonic mechanism) is required to overcome
the non-determinism at the algorithmic level. In Ball
(2007a, 2011), a language processing model based on a pseudo-deterministic,
serial processing mechanism operating over
a probabilistic, parallel processing substrate was described.
A basic element of the serial processing subsystem is a
mechanism of context accommodation wherein the current
input is accommodated without backtracking, if need be.
For example, in the incremental processing of the airspeed
restriction, when airspeed is processed it is integrated as
the head of the nominal the airspeed, but when
restriction is subsequently processed, airspeed is
moved into a modifier function, allowing restriction to
function as the head of the airspeed restriction.
Interestingly, context accommodation gives the appearance
of parallel processing within a serial processing
mechanism (i.e. at the end of processing, it appears that
airspeed was considered a modifier all along). Context
accommodation is a cognitively plausible alternative to the
less cognitively plausible lookahead mechanism of the
Marcus parser (and the cognitively implausible mechanism
of algorithmic backtracking). There is little psychological
evidence that humans are aware of the right context of the
current input (cf. Kim, Srinivas & Trueswell, 2002) as is
strongly implied by a lookahead mechanism. In support of
his lookahead mechanism, Marcus (1980) argues that
strict determinism which eschews all non-determinism
cannot be achieved without it. The context accommodation
mechanism violates Marcus' notion of strict determinism
in that it allows for the modification of existing structure
and is nonmonotonic (i.e. capable of simulating non-determinism),
but whereas exploring the feasibility of strict
determinism for language processing may have been an
important goal of Marcus' research, its reliance on a
lookahead capability appears not to be cognitively viable —
the right context of the input is simply not available to the
human language processor (patently so in spoken
language). Besides the context accommodation
mechanism, the parallel, probabilistic spreading activation
mechanism violates Marcus' notion of strict determinism.
However, parallel processes are well attested in human
cognitive and perceptual processing, and are well
motivated for handling non-determinism
probabilistically — especially at lower levels of cognitive
processing like word recognition and grammatical
construction selection. At Marr's algorithmic level, a non-deterministic
language processing system may still be
cognitively plausible and capable of operating in real-time
if the non-determinism can be handled using parallel,
probabilistic processing mechanisms. The ACT-R
cognitive architecture, which is based on 40+ years of
cognitive psychological research, provides just this
combination of a serial, feed-forward production system
combined with a parallel, probabilistic spreading activation
mechanism which provides the context for production
selection and execution.
Another important advantage of the ACT-R cognitive
architecture is that is provides a virtual machine (i.e. the
cognitive architecture) which supports an executable
algorithmic level description of solutions to complex
cognitive problems (i.e. the cognitive model). The ACT-R
virtual machine also provides execution time information
which makes it possible to determine if the cognitive
model is capable of operating in real-time at the
algorithmic level. The execution of the language
analysis model — which contains 58,000
lexical items — demonstrates that it is capable of operating
incrementally in real-time at the algorithmic level (i.e. the
performance of the model does not degrade with the length
of the input). In addition, the language analysis
model currently operates near real-time on the
implementation hardware. However, the language analysis
model does not yet generate
representations of meaning comparable to humans,
currently generating only a linguistic representation
that can be mapped into a Situation Model for a single domain. The
model also does not make extensive use of the parallel,
spreading activation mechanism which is computationally
explosive on serial hardware. Instead, Double-R relies
on a disjunctive retrieval mechanism (Freiman & Ball, 2008)
that avoids the need to
compute the activation of 58,000 lexical items on each lexical retrieval.
Two examples of computational linguistic systems which
take into consideration cognitive plausibility are the
Eager parser of Shen & Joshi (2005) and the
supertagger of Kim, Srinivas & Trueswell (2002). The
Shen & Joshi parser is designed to be incremental and only
considers the left context in making parsing decisions.
However, this parser performs less well than a less
cognitively plausible bi-directional parser to which it is
compared in Shen (2006). The supertagger of Kim,
Srinivas & Trueswell is concerned with modeling several
psycholinguistic phenomena in a large-scale system based
on a Constraint-Based Lexicalist (CBL) theory of human
sentence processing. Operating incrementally, left-to-right,
the trained connectionist model selects the sequence of
supertags (i.e. lexically specific syntax treelets) which is
most consistent with the input — where supertag selection is
based on a linking hypothesis involving the mapping of the
activated output units to the supertag which is most
consistent with them. The theoretical mechanism by which
the selected supertags are integrated — within the parallel
CBL framework — is not explored.
Most large-scale computational linguistic systems perform
only low-level linguistic analysis of the input. As Shen
Most of the current research on statistical
NLP is focused on shallow syntactic analysis, due to the
difficulty of modeling deep analysis with basic statistical
Building a language comprehension
system based on existing computational linguistic techniques
will require extensive modification to make the system
capable of comprehending language as humans do.
Pitfalls of a Functionalist Approach
A primary risk of a functionalist approach to research is
that it can become largely detached from empirical reality.
This appears to be what has happened in generative
grammar following the ill-advised introduction of
functional heads (cf. Abney, 1987; for a critique, see Ball,
2007b). Recent linguistic representations within generative
grammar do not pass the face validity test — they are too
complex and unwieldy, with too many levels and hidden
elements, to be cognitively plausible. These complex
representations have been motivated on functional grounds
stemming from requirements for increasing the
grammatical coverage to an ever wider range of linguistic
phenomena while at the same time providing a maximally
general theory. The primary empirical methodology
driving generative grammar is judgements of
grammaticality — often by the generative grammarian him
or herself. While grammaticality judgements may be a
reasonable (gross level) empirical method — if applied
judiciously — the cognitive implausibility of the proposed
representations suggests the need for alternative empirical
methods of validation.
On the basis of grammaticality judgments on ever more
esoteric linguistic expressions, more and more linguistic
mechanisms and entities have been proposed within
generative grammar for which there is no explicit evidence
in the linguistic input. The introduction of all these implicit
linguistic entities and mechanisms created a challenge for
theories of language acquisition and led to a reformation of
opinion within generative grammar with the introduction
of the Minimalist Program (Chomsky, 1995). The
Minimalist Program is (in part) an attempt to simplify
generative grammar (in the pursuit of a perfect
computational system), reducing the number of implicit
linguistic entities. Unfortunately, although the Minimalist
Program has been very successful in reducing linguistic
entities and mechanisms, as Culicover & Jackendoff
(2005) argue, it has done so at the expense of being able to
model the broad range of linguistic phenomena covered in
earlier generative theories. Essentially, the Minimalist
Program has defined away all the linguistic variability that
it no longer attempts to model, making that variability
external to the core grammar that is of theoretical
interest. The Minimalist Program has thereby renounced
most functionalist claims in pursuit of a perfect system
of core grammar. The result is a system that is functionally
and empirically incomplete. In pursuit of explanatory
adequacy (how language can be learned), the Minimalist
Program has de-emphasized descriptive adequacy, pushing
many linguistic phenomena to the uninteresting periphery.
In Tenenbaum's (2007) terms, it is a simpler theory which
is a poor fit to much of the available linguistic data.
Culicover & Jackendoff (2005) provide an alternative
within generative grammar called the Simpler Syntax
which retains a strong functionalist orientation while at the
same time challenging the proliferation of linguistic
entities and mechanisms within the syntactic component of
non-minimalist generative grammar. Essentially, the
syntactic component is simplified by introducing a
compositional semantic component with which the
syntactic component interfaces. The syntactic component
is no longer required to support all the grammatical
discriminations that need to be made without recourse to
semantic information (although semantic information is
still isolated in a separate component). Chater &
Christiansen (2007) contrast the simplicity of the
Minimalist Program and the Simpler Syntax, favoring the
Double-R is founded on a linguistic theory (Ball, 2007b) which goes
a step further in arguing that the functional need for a
distinct syntactic component and purely syntactic
representations can be disposed of in favor of linguistic
representations and mechanisms which integrate structural,
functional and grammatically relevant semantic
information — although it has not yet been demonstrated
that the model can cover the full range of linguistic
phenomena addressed in the non-computational theory of
Culicover and Jackendoff. As the rise of the Minimalist
Program and the Simpler Syntax demonstrate, it is
important to reevaluate purported functional mechanisms
in light of theoretical and empirical advances, applying
Ockham's razor judiciously.
Although it is important for a functionalist approach to be
theoretically and empirically validated at reasonable points
to avoid the proliferation of functional entities, it should be
noted that the small-scale empirical method is not
impervious to the proliferation of functional elements that
threatens the functionalist approach. As Gray (2007) notes,
the divide and conquer approach of experimental
psychology has led to a proliferation of purported
mechanisms within individual cognitive subsystems
without due consideration of how these purported
mechanisms can be integrated into a functional cognitive
system. It is avoidance of this proliferation of mechanisms
within individual subsystems that presumably motivates
the minimalist bent within the development of ACT-R.
Alternative cognitive modeling environments like
COGENT (Cooper, 2002) are non-minimalist in that they
support the exploration of multiple cognitive mechanisms
without necessarily making a commitment to a coherent set
of mechanisms for the architecture as a whole. It might be
thought that COGENT would be a more compliant
architecture for building functional systems. However, to
the extent that a functional cognitive model needs to be
coherent, COGENT functions more like a programming
language and less like a cognitive architecture than ACT-R.
The trade-off is an important one. The coherency of
ACT-R constrains the range of possibilities for cognitive
models more so than COGENT. Such constraint is
functional if it pushes model development in the direction
of likely solutions to complex cognitive problems without
being overly constraining. As I have argued elsewhere
(Ball, 2006), I view the constraints provided by ACT-R as
largely functional and I consider the current level of
success of Double-R to have been
facilitated by the ACT-R cognitive architecture.
Besides a functionalist approach being at risk of becoming
detached from empirical reality, to the extent that a
complex cognitive system is being modeled, there is a risk
of the complexity overwhelming development. It may be
argued that the past failures of explicitly developed NLP
systems have stemmed from the inability to manage this
complexity. At the Cognitive Approaches to NLP AAAI
symposium in fall 2007, Mitchell Marcus argued that
large-scale NLP systems could not be developed without
recourse to automated machine learning techniques.
Indeed, most computational linguistic research aimed at
development of large-scale systems has come to rely on the
use of machine learning techniques. A side effect of this
research direction is that it is more difficult to enforce
cognitive constraints, since the machine learning
computations are outside the direct control of the
researcher. Further, it is not unusual for NLP systems
created using machine learning techniques to contain
thousands (or tens of thousands) of distinct linguistic
categories, many of which have no mapping to commonly
accepted linguistic categories. These systems perform
extremely well on the corpora they were trained on.
However, the underlying models are extremely complex
and it looks suspiciously like they are over fitting the data
(i.e. ignoring Tenenbaum's trade-off between simplicity
and fit). That the test set for such models often comes from
the same corpus as the training set (the annotated Penn
Treebank Wall Street Journal Corpus) does not provide an
adequate test of the generalizability of such models. As
Fong & Berwick (2008) demonstrate, the Bikel
reimplementation of the Collins parser is quite sensitive to
the input dataset, making prepositional phrase attachments
decisions that reflect lexically specific occurrences in the
dataset (e.g. "if the noun following the verb is 'milk' attach
low, else attach high").
The simplest rejoinder to the position put forward by
Marcus is to develop a functionally motivated and explicit
NLP system that proves him wrong. Easier said than done!
Statistical systems developed using machine learning
techniques dominate computational linguistic research
because they outperform competing explicitly developed
functional systems when measured on large annotated
corpora like the Penn Treebank (Marcus, et al., 1993).
However, there are reasons for believing that an explicitly
developed functional system might eventually be
developed which outperforms the best machine learning
systems. In the first place, an explicitly developed
functional system can take advantage of statistical
information. Once an appropriate ontology of linguistic
categories has been functionally identified, statistical
techniques can be used to compute the probabilities of
occurrence of the linguistic categories, rather than using
brute force machine learning techniques to identify the
categories purely on the basis of low-level distributional
information. Instead of having categories like on the and
is a identified on the basis of pure statistical cooccurrence
in unsupervised systems, supervised systems
can use phrase boundaries and functional categories (e.g.
subject, object, head, specifier, modifier) to segment and
categorize word sequences prior to computing cooccurrence
frequencies. Statistical systems based on the
annotated Penn Treebank corpus already make use of
phrase boundary information, but these systems typically
ignore the functional category information (including
traces) provided in the annotations (Manning, 2007;
Gabbard, Marcus & Kulick, 2006 is an exception). In
general, the more high level functional information that
can be incorporated into the supervised machine learning
system, the better. The value of doing so is a more
coherent system. Low level statistical regularities may be
useful for low level linguistic analyses like part of speech
tagging (and maybe even syntactic parsing), but to the
extent that they are not functionally motivated, they are
likely to impede the determination of higher level
A good way to overcome complexity is to base
development on a sound theory (back to Marr). The failure
of earlier functional NLP systems may be due in large part
to the weak or inappropriate linguistic representation and
processing theory on which they were based. Staged
models of language processing with autonomous lexical,
syntactic, semantic and pragmatic components were never
practical for large-scale NLP systems. The amount of nondeterminism
they engender is fatal. For a system to be
pseudo-deterministic, it must bring as much information
to bear as possible at each decision point. The system must
be capable of making the correct choice for the most part,
otherwise it will be overwhelmed. The system must not be
based on a strong assumption of the grammaticality of the
input, nor assume a privileged linguistic unit like the
sentence will always occur. Yet these are all typical
assumptions of earlier systems which are often violated by
the linguistic input. Psycholinguistics is currently
dominated by a number of constraint based theories of
language processing. These theories are largely valid,
however, they tend to ignore the overriding serial nature of
language processing. There must be some serial selection
and integration mechanism operating over the parallel
substrate of constraints, lest the system be incapable of
making decisions until the entire input has been processed.
Carrying multiple choices forward in parallel is only
feasible if the number of choices selected at each choice
point is kept to a minimum, preferably one, very
infrequently more. Otherwise, the number of choices will
proliferate beyond reasonable bounds and performance will
degrade with the length of the input. Parallel, constraint
based psycholinguistic models typically focus on the
choice point of interest, often ignoring the possibility of
other choice points (cf. Kim, Srinivas & Trueswell, 2002)
and delaying selection until the end of the input when all
constraints have had their effect (typically within a
connectionist network). Even the large-scale system of
Kim, Srinivas and Trueswell (2002) leaves unexplained
how the supertags get incrementally integrated. Parallel
computational linguistic systems — which cannot assume
away choice points — typically impose a fixed-size beam
on the number of choices carried forward, often much
larger than is cognitively feasible to reduce the risk of
pruning the correct selection before the end of the input
when all co-occurrence probabilities can be computed.
In an integrated system it is possible to ask what is driving
the interpretation of the current input and encode that
information into the system. Is it the previous word which
forms an idiom with the current word? Is it the part of
speech of the previous word which combines with the part
of speech of the current word to form a phrasal unit? Is it
the preceding phrasal unit which combines functionally
with the phrasal unit of the current word to form some
higher level functional category? Utilities can be assigned
to the different possibilities and the assigned utilities can
be tested out on a range of different inputs to see if the
system performs the appropriate integration in different
contexts. If not, the system can be adjusted, adding
functional categories as needed to support the grammatical
distinctions that determine appropriate structures. For
example, the word the, a determiner, is a strong
grammatical predictor of a nominal. To model this, allow
the to project a nominal construction, setting up the
expectation for the head of the nominal to follow. On the
other hand, the word the is a poor grammatical predictor
of a sentence. Unlike left-corner parsers which typically
have the project a sentence for algorithmic reasons, wait
for stronger grammatical evidence for a sentence (or
clause). If the word red follows the, in the context of
the and the projected nominal, red is a strong predictor
of a nominal head modifier. Allow the adjective red to
project a nomimal head with red functioning as a
modifier of the head and predicting the occurrence of the
head. If the word is follows, is is a strong predictor of
a clause. Allow is to project a clause with a prediction
for the subject to precede the auxiliary is and a clausal
head to follow. Since the nominal the red has been
projected, allow the red to function as the subject, even
though a head has not been integrated into the nominal.
Note that the words the red are sufficient to cause human
subjects to look for red objects in a visual scene in Visual
World Paradigm experiments (e.g. Tanenhaus et al.,
1995) — providing strong evidence for the incremental and
integrated nature of language comprehension. Further, if
there is only one red object, the red suffices to pick it out
and the expression is perfectly intelligible, although
lacking a head (and it is certainly a nominal despite the
lack of a head noun). If the word nice follows is, in the
context of is and the projected clause, allow the adjective
nice to function as the clausal head. Let the lexical items
and grammatical cues in the input drive the creation of a
linguistic representation (cf. Bates & MacWhinney, 1987).
When processing a simple noun like ball, in the absence
of a determiner, allow ball to project a nominal in which
it functions as the head. Both a determiner and a noun (in
the absence of a determiner) are good predictors of a
nominal, but they perform different functions within the
nominal (i.e., specifier vs. head). Both an auxiliary verb
and a regular verb (in the absence of an auxiliary verb) are
good predictors of a clause. Allow the grammatical cues in
the input and a functional ontology to determine which
higher level categories get projected. This is the basic
approach being followed in the language analysis model
A cognitively plausible, functional approach to the modeling of
language comprehension has much to recommend it.
Adhering to cognitive constraints on language processing
moves development in directions which are more likely to
be successful at modeling human language processing
capabilities than competing approaches. Modeling a
complex cognitive system has the potential to overcome
the functional shortcomings of small-scale cognitive
modeling research in addressing Newell's 20 questions
critique. However, from the perspective of cognitive
modeling, the approach may appear to be insufficiently
grounded in empirical validation, and from the perspective
of computational linguistics, the approach may appear to
be computationally naïve and unlikely to succeed. What is
needed is a demonstration that the approach is capable of
delivering a functional system that is cognitively plausible.
Lacking that demonstration, one can only conjecture about
the feasibility of the methodology proposed in this section.
It is hoped that Double-R Grammar will soon provide that
Chapter 3: Pseudo-Deterministic Human Language Processing
The theoretical commitments underlying Double-R align with current
linguistic theory in Cognitive Grammar (Langacker, 1987,
1991), Sign-Based Construction Grammar (Sag, 2010) and
Conceptual Semantics (Jackendoff, 2002), and borrow
ideas from Preference Semantics (Wilks, 1975) and Tree
Adjoining Grammar (Joshi, 1987). A key goal of the
research is development of a functional model that adheres
to well-established cognitive constraints. Such constraints
have evolved to be largely functional in humans (Ball et al.,
2010). Double-R also borrows heavily from the
comprehensive grammar of Huddleston & Pullum (2002,
2005) and the Simpler Syntax of Culicover & Jackendoff
(2005; Culicover, 2009). A key feature of the grammar of
Huddleston & Pullum (henceforth H&P) is the introduction
of phrase internal grammatical functions like head,
determiner (or specifier) and modifier. Lexical items and
phrases may have alternative functions in different
grammatical contexts. For example, a prepositional phrase
may function as a modifier (or adjunct) in one context (e.g.
He will eat dinner in a minute), and as a verbal
complement in a different context (e.g. He put the book on
the table). Although the typical subject (a clause level
grammatical function) is a noun phrase, various clausal
forms can also function as subject (e.g. That he likes you is
true, Going to the movies is fun).
Differences from these grammatical treatments are
largely motivated by constraints imposed by the incremental
and interactive nature of HLP as reflected in the
computational implementation. For example, wh-words
occurring at the beginning of a sentence are uniformly
assigned a wh-focus function that is distinct from the
subject function. In Who is he talking to?, who
functions as the wh-focus and he functions as the subject
of the wh-question construction that is projected during the
processing of who is…. In addition, who is secondarily
bound to the object function of the locative construction
projected during processing of the preposition to.
Likewise, in Who is talking?, who again functions as
the wh-focus, but in this case who is secondarily bound to
the subject function. In contrast, H&P treat who as the
subject in Who is talking? and as a pre-nucleus which is
external to the main clause in Who is he talking to?.
However, at the processing of who in an incremental
processor, it is not possible to determine which function
applies given the H&P grammar, whereas who is
uniformly treated as the wh-focus in the pseudo-deterministic
model. Further, the pseudo-deterministic
model projects a uniform wh-question construction with
both a wh-focus and subject function (allowing the subject
to be bound to the wh-focus), whereas the grammar of H&P
needs two different representations: one with a clause
external pre-nucleus when the wh-word is not the subject,
and one that is a simple clause when the wh-word is the
subject. An incremental processor would need to project
both alternatives in parallel to be able to efficiently process
wh-questions beginning with who. Although this is
possible, parallel projection of alternative structures must be
highly constrained to avoid a proliferation of alternatives
within the serial processing mechanism which has limited
capacity to maintain alternative structures in parallel.
Parallel, Probabilistic Activation and Selection
Based on the current input (constrained to a 12 character perceptual window),
current context and prior history
of use, a collection of DM elements is activated via the
parallel, spreading activation mechanism of ACT-R. The
selection mechanism is based on the retrieval mechanism of
ACT-R. Retrieval occurs as a result of selection and
execution of a production
— only one production can be
executed at a time
— whose right-hand side provides a
retrieval template that specifies which type of DM chunk is
eligible to be retrieved. The single, most highly activated
DM chunk matching the retrieval template is retrieved.
Generally, the largest DM element matching the retrieval
template will be retrieved, be it a word, multi-unit word
(e.g. a priori, none-the-less), multi-word expression
(e.g. pick up, go out), or larger phrasal unit.
To see how the spreading activation mechanism can bias
retrieval, consider the processing of the speed vs. to
speed. Since speed can be both a noun and a verb, we
need some biasing mechanism to establish a context
sensitive preference. In these examples, the word the
establishes a bias for a noun to occur, and to establishes a
bias for a verb to occur (despite the ambiguity of to itself).
These biases are a weak form of prediction. They differ
from the stronger predictions that result from projection of
constructions from lexical items, although in both cases the
prediction may not be realized. In addition to setting a bias
for a noun, the projects a nominal construction which
establishes a prediction for a head, but does not require that
this head be a noun. If the is followed by hiking,
hiking will be identified as a present participle verb since
there is no noun form for hiking in the mental lexicon.
There are two likely ways of integrating hiking into the
nominal construction projected by the:
Since it is not possible to know in advance which
structure will be needed, Double-R must chose one and be
prepared to accommodate the alternative (accommodation
may involve parallel projection of the alternative). Based on
history of use (derived from the Corpus of Contemporary
American English), hiking has a strong preference to
function as a nominal head, so Double-R initially treats
hiking as the head and accommodates shoes in the same
way as noun-noun combinations (discussed below). This is
in contrast to adjectives which have a strong preference to
function as modifiers in nominals. Adjectives project a
structure containing a pre-head modifying function and
head, with the adjective integrated as the modifier and a
prediction for a subsequent head to occur.
- hiking can be integrated as the head as in the hiking of Mt. Lemmon
- hiking can project a modifying structure and set up the
expectation for a head to be modified as in the hiking
Although the parallel, probabilistic mechanism considers
multiple alternatives in parallel, the output of this parallel
mechanism is a single linguistic unit. For motivation at the
lexical level, consider the written input car. Although this
input may activate lots of words in memory, ultimately, the
single word car is brought into the focus of attention
(retrieved from memory and put in the retrieval buffer in
ACT-R terms). If instead, the input is carpet or
carpeting, a single, but different, word enters the focus of
attention. If car were initially retrieved during the
processing of car… (perhaps more likely in the case of
spoken input), then it is simply overridden in the focus of
attention if the input turns out to be carpet. Likewise for
carpet… if it turns out to be carpeting. The processing
of carpeting does not lead to car, carp, pet, and
carpet all being available in the focus of attention along
with carpeting (although these words may all be activated
in DM). The single word that is most consistent with the
input enters the focus of attention.
Serial, Pseudo-Deterministic Structure Building and Context Accommodation
To capture the incremental and immediate nature of HLP,
we propose a serial, pseudo-deterministic processor that
builds and integrates linguistic representations, relying on a
non-monotonic mechanism of context accommodation with
limited parallelism, which is part of normal processing, to
handle cases where some incompatibility that complicates
integration manifests itself.
The primary monotonic mechanisms for building
structure within the serial mechanism include:
For example, given the input
the pilot, the processing of the will lead to projection of
a nominal construction and integration of the as the
specifier of the nominal. In addition, the prediction for a
head to occur will be established.
When pilot is subsequently processed, it is biased
to be a noun and integrated as the head of the nominal
construction projected by the.
of the current input into an existing construction which
predicts its occurrence (substitution)
- projection of a
new construction and integration of the input into this
construction (Ball, 2007b).
Besides predicting the occurrence of an upcoming
linguistic element, projected constructions may predict the
preceding occurrence of an element. If this element is
available in the current context, it can be integrated into the
construction. For example, given the pilot flew the
airplane, the processing of flew will lead to projection of
a declarative clause construction which predicts the
preceding occurrence of a subject. If a nominal is available
in the context (as in this example), it can be integrated as the
subject of the declarative clause construction.
In addition to these monotonic mechanisms, a projected
construction may non-monotonically override an existing
construction. For example, in the processing of
the pilot light, the incremental integration of pilot as the
head of the nominal construction will subsequently be
overridden by a construction in which pilot functions as a
modifier and light functions as the head.
The structure building mechanism involves the serial
execution of a sequence of productions that determine how
to integrate the current linguistic unit into an existing
representation and/or which kind of higher level linguistic
structure to project. These productions execute one at a time
within ACT-R, which incorporates a serial bottleneck for
The structure building mechanism uses all available
information in deciding how to integrate the current
linguistic input into the evolving representation. The
mechanism is deterministic in that it builds a single
representation which is assumed to be correct, but it relies
on the parallel, probabilistic mechanism to provide the
inputs to this structure building mechanism. In addition,
structure building is subject to a mechanism of context
accommodation capable of making modest adjustments to
the evolving representation. Although context
accommodation is part of normal processing and does not
involve backtracking or reanalysis, it is not, strictly
speaking, deterministic, since it can modify an existing
representation and is therefore non-monotonic.
Context accommodation makes use of the full context to
make modest adjustments to the evolving representation or
to construe the current input in a way that allows for its
integration into the representation. It allows the processor to
adjust the evolving representation without lookahead,
backtracking or reanalysis, and limits the need to carry
forward multiple representations in parallel or rely on delay
or underspecification in many cases.
As an example of accommodation via
In this example, hiking
is construed objectively and integrated as the head of a nominal
even though it is a present participle verb.
As an example of accommodation via function
When airspeed is processed, it is integrated as
the head of the nominal projected by the. When
restriction is subsequently processed, there is no
prediction for its occurrence. To accommodate restriction,
airspeed must be shifted into a modifying function to
allow restriction to function as the head. This function
shifting mechanism can apply iteratively as in the
where screw is the ultimate head of the nominal, but pressure,
valve and adjustment are all incrementally integrated as
the head prior to the processing of screw. Note that at the
end of processing it appears that pressure, valve and
adjustment were treated as modifiers all along, giving the
appearance that these alternatives were carried along in
parallel with their treatment as heads.
- the pressure valve adjustment screw
At a lower level, there are accommodation mechanisms
for handling conflicts in the grammatical features associated
with various lexical items. For example, the grammatical
number feature singular is associated with a and the
number feature plural is associated with few and pilots.
the singular feature of a is overridden
by the plural feature of few and pilots and the nominal
is plural overall (Ball, 2010a).
The preceding text argued for a parallel mechanism for
selecting between competing structures combined with a
serial mechanism for building structure given the parallel
selection. The architectural mechanism which supports
selection is ACT-R’s DM retrieval mechanism which
returns a single structure. However, is it always the case that
the input to the serial, structure building mechanism is a
single structure? Just & Carpenter (1992) provide evidence
that good readers (among CMU subjects) can maintain two
alternative (syntactic) representations of ambiguous inputs
in parallel during the processing of sentences which may
contain a dispreferred reduced relative clause
- the experienced soldiers warned about the dangers conducted the midnight raid
good readers are limited to a single representation. So long
as the preferred representation at the verb (i.e., the main
verb reading) is ultimately correct, less good readers do well
relative to good readers. But if the preferred representation
at the verb is incorrect for a given input, less good readers
do significantly worse than good readers at the point of
disambiguation (i.e. less good readers are garden-pathed).
However, according to the authors,
- the experienced soldiers warned about the dangers before the midnight raid
multiple representations of a syntactic ambiguity is so
demanding that it produces a performance deficit, which is
shown only by the good readers (ibid, p. 131)
Good readers are slower on ambiguous inputs vs. unambiguous
— e.g. the soldiers warned... vs. the soldiers
— relative to less good readers.
Reduced relative clauses are special constructions which
have generated a large amount of psycholinguistic research.
Bever's (1970) famous example of a garden-path The
horse raced past the barn fell stumps even good readers.
Garden-path effects are explained as a disruption of normal
processing requiring introduction of reanalysis mechanisms.
Such disruption should not occur if competing alternatives
are available in parallel. Other types of garden-path inputs
exist. A classic example is the old train the young (Just &
Carpenter, 1987). The garden-path effect after train
suggests that readers make a strong commitment to use of
train as a noun and do not have parallel access to the
strongly dispreferred verb use during normal processing of
this simple sentence. It is especially revealing that the
garden-path effect occurs immediately after the processing
of train, implying severe limits on parallel structures.
However, there are examples of the need for parallelism
in structure building which have small but cumulative
effects on normal processing (Freiman & Ball, 2010). Such
examples provide evidence for a mechanism like context
accommodation combined with a limited capacity to
maintain multiple structures in parallel for efficiency.
We have already briefly discussed the example the
airspeed restriction where it was suggested that the
processing of restriction causes airspeed to be shifted
into a modifying function to allow restriction to be the
head. There are at least three mechanisms for achieving this within
the constraints of ACT-R. The first approach involves
parallel projection of the structure needed to support the
accommodation at the time airspeed is processed. The
second approach involves projection of the needed structure
at the processing of restriction. The third approach involves
making the extra structure globally accessible.
In the first approach, the
processing of airspeed leads to its integration as the head
of the nominal projected by the. In parallel, a structure
which supports both a pre-head modifier and head is
projected and made separately available (called an obj-head in Double-R). When restriction
is processed, the initial integration of airspeed as the head
of the nominal is overridden by this alternative structure.
Within this structure, airspeed is shifted into the
modifying function and restriction is integrated as the
head. In ACT-R, this is accomplished in a single
computational step via execution of a production which
makes the needed adjustments. In the second approach,
when restriction is processed in the context of the
airspeed, a structure with a pre-head modifier function, in
addition to a head, is projected. Restriction is integrated
as the head of this structure and airspeed is shifted into
the modifying function. This new structure then overrides
airspeed as the head of the nominal. Within ACT-R, the
second approach requires an additional computational step
relative to the first approach. It is not possible to project the
— which requires creation or retrieval of a DM chunk
— and integrate that structure into another
structure in a single procedural step. To avoid this extra
computational step and bring Double-R into closer
alignment with adult human reading rates (Freiman & Ball,
2010), Double-R originally adopted the first approach, and has more recently
switched to the third approach. The rapidity
with which humans process language (200-300 wpm for
fluent adult readers) suggests that humans can learn to
buffer needed information for efficiency. That information can be buffered
either globally or locally (as needed). Global buffering of information
has the advantage of simplifying local processing. When a noun is processed,
it can be integrated into an object referring expression without the need
to project an alternative object head (as in the first approach),
since an object head with empty grammatical functions is globally available.
When the globally accessible object head is used and the grammatical functions
instantiated, the globally accessible object head need only be replaced with another
uninstantiated object head. Currently, Double-R makes use of globally accessible
structures to support the efficient processing of optional elements (e.g. modifiers), and relies
on local projection for the processing of non-optional elements (e.g. arguments).
If the globally accessible object head structure
supports both a pre- and post-head modifier, then post-head
modifiers can also be accommodated. For example, in the
book on the table, if integration of book as the head of
the nominal projected by the occurs in parallel with
a globally accessible object head structure with a prediction for a post-head
modifier, then this structure can override the treatment of
book as the head when a post-head modifier like on the
table occurs. The primary alternative is to have the post-head
modifier project the structure needed to accommodate
both the head and the post-head modifier, and then override
the previous head. Within ACT-R, this latter approach
requires an extra computational step and is less efficient.
As another example of the need for context
accommodation in incremental HLP, consider the
processing of ditransitive verb constructions. Given the
input he gave the…, the incremental processor doesn't
know if the is the first element of the indirect or direct
object. In he gave the dog the bone, the introduces the
indirect object, but in he gave the bone to the dog, it
introduces the direct object. How does the HLP proceed?
Delay is not a generally viable processing strategy since the
amount of delay is both indeterminate and indecisive as
In 1, the inanimacy of bone, the head of the nominal,
suggests the direct object as does the occurrence of to the
dog which is the prepositional form of the indirect object,
called the recipient in Double-R. In 2, the animacy of dog
in the first nominal, and the inanimacy of bone in the
second nominal suggest the indirect object followed by the
direct object. Delaying until the head occurs would allow
the animacy of the head to positively influence the
integration of the nominal into the ditransitive construction
in these examples. However, in 3, the animacy of dog also
suggests the indirect object, but dog turns out not to be the
head. In 4, the animacy of dog which is the head, suggests
the indirect object, but this turns out not to be the case given
the subsequent occurrence of the recipient to me. There
are just too many alternatives for delay to work alone as an
effective processing strategy. Although there are only two
— indirect object followed by direct object
or direct object followed by recipient
— which outcome is
preferred varies with the current context and no alternative
can be completely eliminated. And there is also a
dispreferred third alternative in which the direct object
occurs before the indirect object as in he gave the bone the
dog. In Double-R, ditransitives are handled by projecting
an argument structure from the ditransitive verb which
predicts a recipient in addition to an indirect and direct
object (this might be viewed as a form of
underspecification). Although it is not possible for all three
of these elements to occur together, it is also not possible to
know in advance which two of the three will be needed. So
long as Double-R can recover from an initial mistaken
analysis without too high a cost, early integration is to be
preferred. Currently, Double-R projects a nominal from
the following the ditransitive verb and immediately
integrates the nominal as the indirect object of the verb.
Once the head of the nominal is processed, if the head is
inanimate, the nominal is shifted to the direct object. If the
first nominal is followed by a second nominal, the second
nominal is integrated as the direct object, shifting the
current direct object into the indirect object, if necessary.
This argument shifting is in the spirit of slot bumping as
advocated by Yorick Wilks (p.c.). If the first nominal is
followed by a recipient to phrase, the first nominal is
made the direct object, if need be. If the first nominal is
inanimate and made the direct object and it is followed by a
second nominal that is animate, the second nominal is
integrated as the indirect object. It is important to note that
the prediction of all three elements by the ditransitive verb
supports accommodation at no additional expense relative to
a model that predicted only one or the other of the two
primary alternatives. However, unlike a model where one
alternative is selected and may turn out to be incorrect,
necessitating retraction of the alternative, there is no need to
retract any structure when all three elements are
simultaneously predicted, although it is necessary to allow
for a prediction to be left unsatisfied and for the function of
the nominals to be accommodated given the actual input.
- he gave the very old bone to the dog
- he gave the verb old dog the bone
- he gave the very old dog collar to the boy
- he gave the old dog on the front doorstep to me
The processing of ditransitive verbs is complicated
further within a relative clause construction which contains
an implicit complement (either the object or indirect object)
that is bound to the nominal head. Consider
In 5, the book is bound to the implicit object of gave within the
relative clause based on the inanimacy of book. In 6,
the man is bound to the implicit indirect object of gave based on the
animacy of man. Note that animacy is the determining
factor here. There is no structural distinction to support
these different bindings. These bindings are established at
the processing of gave without delay when the ditransitive
structure is first projected. In 7, the man is initially bound to
the indirect object, but this initial binding must be adjusted
to reflect the subsequent occurrence of to which indicates
a recipient phrase even though no explicit object follows the
- the booki that I gave the man obji
- the mani that I gave iobji the book
- the mani that I gave the book to obji
Things get even more interesting if we combine a
ditransitive verb construction with a wh-question and
passive construction. Consider
In this case, neither the object nor indirect object of given
occurs in canonical position within the ditransitive verb
construction. In this example, the wh-focus what is bound
to the implicit object, and the subject he is bound to the implicit indirect
object. Again, the inanimacy of what and the animacy of
he are the determining factors.
- whati could hej have been given
As a final example, consider the processing of the
ambiguous word to. Since to can be both a preposition
(e.g. to the house) and a special infinitive marker (e.g. to
speed) it might seem reasonable to delay the processing of
to until after the processing of the subsequent word.
However, to provides the basis for biasing the subsequent
word to be an infinitive verb form (e.g. to speed vs. the
speed) and if its processing is delayed completely there
will be no bias. How should the HLP proceed? If the context
preceding to is sufficiently constraining, to can be
disambiguated immediately as when it occurs after a
ditransitive verb (e.g. He gave the bone to…). Lacking
sufficient context, to can set a bias for an infinitive verb
form to follow even though the processing of to is itself
delayed until after the next word is processed. This is the
default behavior of Double-R. However, Double-R also
supports the recognition of multi-word units using a
perceptual span for word recognition that can overlap
multiple words (Freiman & Ball, 2010). With this
perceptual span capability, an expression like to speed can
be recognized as a multi-word infinitival unit and the
processing of to need not be delayed in this context.
Similarly, to the can be recognized as a prepositional
phrase lacking a nominal head. Although not typically
considered a grammatical unit in English, to the is
grammaticalized as a single word form in some romance
languages and its frequent occurrence in English suggests
unitization. The perceptual span is roughly equivalent to
having a limited lookahead capability. Overall, the
processing of to encompasses a range of different
mechanisms that collectively support its processing. Some
of these mechanisms are specific to to, and others are
Summary & Conclusions
This section proposes, empirically motivates and describes
the implementation of a pseudo-deterministic model of
HLP. The use of the term pseudo-deterministic reflects the
integration of a parallel, probabilistic activation and
selection mechanism, and non-monotonic context
accommodation mechanism (with limited parallelism), with
what is otherwise a serial, deterministic processor. The
serial mechanism proceeds as though it were deterministic,
but accommodates the changing context, as needed, without
backtracking and with limited parallelism, delay and
underspecification. The overall effect is an HLP which
presents the appearance and efficiency of deterministic
processing, despite the rampant ambiguity which makes
truly deterministic processing impossible.
Extended Example Representation and Processing
This section presents an extended example that highlights many of the grammatical characteristics of Double-R.
The rest of the document provides a phased introduction to these characteristics and more.
The representations shown below and in the rest of this document were generated automatically
by Double-R and are not hand-crafted.
What could he have been given to be eaten?
This diagram shows the integration of several different constructions and demonstrates many of the features of the grammar:
- Wh-question Construction (wh-quest-sit-refer-expr)
- wh-focus (what)
- wh-focus (what) provides binding for trace object of given
- operator (could)
- subject (he)
- specifier (have been)
- head (given)
- grammatical features
- tense-1 = finite (from could)
- tense = present (from could)
- aspect = perfect (from been, given)
- voice = passive (from given)
- modality = could (from could)
- discourse function (df) = question
- bind index
- Passive Construction (been given)
- subject (he) provides binding for trace indirect object of given
- Ditransitive Verb Construction (pred-ditrans-verb)
- head (given)
- trace indirect object bound to subject
- trace object bound to wh-focus
- complement (to be eaten)
- Infinitive Construction (inf-sit-refer-expr)
- PRO subject bound to matrix object which is bound to wh-focus
- specifer (to be)
- head (eaten)
- grammatical features
- tense-1 = non-finite (from to be)
- aspect = perfect (from eaten)
- voice = passive (from eaten)
- bind index
- Passive Construction (to be eaten)
- embedded PRO subject (bound to matrix object which is bound to wh-focus) provides binding for trace embedded object
- Transitive Verb Construction (pred-trans-verb)
- head (eaten)
- trace object bound to PRO subject which is bound to matrix object which is bound to wh-focus
- Nominal Construction (wh-obj-refer-expr)
- head (what)
- grammatical features
- definiteness = wh-definite
- number = singular
- animacy = inanimate
- bind index
- Nominal Construction (pron-obj-refer-expr)
- head (he)
- grammatical features
- definiteness = definite
- number = singular
- animacy = human
- gender = male
- person = third
- case = subjective
- bind index
- Multi-word Auxiliary (aux+aux have been)
Double-R representations follow the grammar of Huddleston & Pullum (2002) in encoding
both the grammatical type (or category) and the grammatical function of words and expressions.
For example, the wh-word what has the grammatical type of
a wh-nominal-determiner (wh-nominal-det)
and functions as the grammatical head of
a wh object referring expression (wh-obj-refer-expr).
The grammatical type is a part of speech in the case of words. The part of speech
of what is a composite part of speech that combines the wh-nominal part of speech
with the determiner part of speech. Wh-nominal is
a subtype of nominal (i.e. a word that functions like a nominal expression).
This composite part of speech captures the full range of behavior of what
Wh object referring expression is a phrase level grammatical type
consisting of the single word what which functions as the wh-focus
of a wh-question construction.
Wh-question construction is a clause (or sentence) level grammatical type.
- what is on the table — what functions like a wh-nominal
- what book is on the table — what functions like a determiner
Grammatical types are organized into a multiple inheritance hierarchy such that the grammar
can represent words and expressions at different levels of abstraction.
(Support for multiple inheritance in ACT-R was provided in December 2013 by Mark Burstein of SIFT.)
For example, wh-nominal-det is a subtype of the more general grammatical types wh-nominal
and determiner, and wh object referring expression is a subtype of
object referring expression and wh referring expression.
For some grammatical purposes (e.g. determining the wh-focus) the grammar needs to know
that a word is a wh-nominal;
for other grammatical purposes (e.g. combining with a noun head) the grammar needs to know
that the word is a determiner.
The hierarchy in Double-R aligns with the hierarchy in
Head-Driven Phrase Structure Grammar (HSPG) (Sag, Wasow & Bender, 2003)
and Sign-Based Construction Grammar (SBCG) (Sag, 2010).
We prefer to use the terms grammatical type and grammatical category over grammatical form
since the form of an expression of a given grammatical type can vary.
This variability in form, combined with downplaying the importance of grammatical function,
can lead to the treatment of words and expressions of radically different forms
as though they were the same because the grammar appears to require it.
As a simple example, in the running bull
and the running of the bull, an approach based on syntactic form
(derived on the basis of purely distributional information) might suggest that
is an adjective in the running bull
and a noun in the running of the bull.
This follows given the assumption that noun phrases (syntactic form)
are necessarily headed by nouns (lexical form)
and noun heads (syntactic form) are necessarily modified by adjectives (which precede the head).
But this syntactic form based categorization is inconsistent with the morphological form of the word
exhibits the progressive verb form ending -ing. In these examples, syntactic form and morphological
form are incompatible.
Both cannot be correct. When grammatical functions are considered, it is easy to see that
functions as the head
in the running of the bull, and as a modifier of the head bull
in the running bull.
There is no need to suggest that running is other than a progressive verb in these examples.
But this means giving up the deeply entrenched notion that noun phrases are syntactic forms that are headed by nouns,
which are lexical forms.
If the head of a noun phrase need not be a noun, then there is no universal basis for
the syntactic form based category noun phrase.
Looked at from a semantic point of view, noun phrases
(better called nominals) are referring expressions
(Lyons, 1977), whether or not they are
headed by nouns. They are expressions that are intended to refer to objects. In the case of an expression like
the running of the bull, the event of running by the bull is objectified
— the overall expression refers to an object
(i.e. a reified event) even though the head running is a verb.
The meaning of running by itself, is not materially changed within this expression.
There is no need to posit an entry in the mental lexicon for a noun version of running.
Doing so would just add ambiguity that complicates processing.
Nor is it desirable to have the word running in the mental lexicon without
a part of speech specification (Borer, 2009, 2011).
Having part of speech information is crucial to language analysis. For example,
if the word the were not categorized as a
determiner, then this information would not be available to help identify an object referring expression when the
is processed. What the mental lexicon needs is information about the part of speech associated
with the most common uses
of a word. When a word is processed, the part of speech for the most common use will be retrieved,
unless the linguistic
context is sufficient to bias retrieval of the part of speech associated with a less common use.
For a word like pilot
which is genuinely ambiguous (i.e. it has multiple meanings), there will be two entries in the mental lexicon,
one corresponding to
the senses which are categorized as a noun, and another corresponding to the sense which is categorized as a verb.
In the context of the
as in the pilot,
language analysis will be biased to retrieving the noun part of speech;
in the context of to
as in to pilot,
language analysis will be biased to retrieving the verb part of speech.
Having an adjective entry
for pilot to handle inputs like the pilot light,
where pilot modifies light, would only complicate processing.
At the incremental processing of pilot, the locally best analysis is that it is a noun functioning as head.
Only when light
is subsequently processed, can it be determined that pilot is functioning as a modifier, not the head.
At this point, the context accommondation mechanism kicks in and shifts pilot, the noun,
from the head to a modifier function
(and adjusts the meaning)
so that light can function as the head. Having an adjective entry for
pilot would complicate rather than
facilitate processing, assuming
an incremental processor as in Double-R, by creating an additional ambiguity at the processing of
that is not associated with any difference in meaning.
Unfortunately, syntactic form based approaches lead to a proliferation of entries, even when
there is no meaning difference.
In Double-R, we avoid introducing ambiguity based on purely structural considerations.
From an incremental processing perspective,
it is crucial to limit the amount of ambiguity in the mental lexicon.
Many of the representational assumptions of Double-R reflect such processing considerations.
In this respect, Double-R differs from most other grammatical formalisms, even grammatical formalisms like HPSG
for which computational implementations are available.
The term construction is used as a synonym for grammatical type in Double-R.
Words are represented as instantiated instances of part of speech constructions (i.e. lexical constructions).
At the phrase, clause and sentence level,
a construction is the specification of an ordered sequence of grammatical functions
followed by an unordered sequences of grammatical features that are abstracted from specific linguistic inputs
during language acquisition.
When the grammatical functions and features in the grammatical construction
are filled in by words and expressions (in the case of grammatical functions)
and feature values (in the case of grammatical features) during language analysis,
the result is a linguistic or grammatical representation
— i.e. an instantiated instance of the grammatical construction.
For example, an object referring expression is a grammatical construction that
specifies the grammatical functions specifier (spec),
head and post-head modifier (post-mod),
and the normal order in which they occur, along with the unordered grammatical features
definiteness (def), distance,
person, number, gender, animacy and case.
The grammatical functions may be filled by a range of words and expressions of different grammatical categories.
The grammatical features have a fixed set of values (e.g. the number feature has the values singular
and plural) (Ball, revised 2013, 2012).
The modifier (mod) and post-head modifier (post-mod)
functions are always optional.
At a minimum, either a specifier (spec) or a head must occur.
If a specifier occurs (e.g. the in the books functions as a specifier), the construction
is projected from the specifier
(i.e. the processing of the leads to projection of
an object referring expression in which the
functions as the specifier).
If no specifier occurs (e.g. books functions as the head in
books), the construction is projected by the head
and the specifier function is empty. Having an empty specifier is the Double-R equivalent of
the MP (Minimalist Program) treatment of books as a DP (determiner phrase) with an empty functional head
(cf. Carnie, 2011),
except that Double-R rejects the functional head hypothesis (Abney, 1987),
claiming instead that the functional head hypothesis is wrong-headed!
This claim is based on a commitment to providing a semantic basis for the head function
The head is the semantically most significant element of an expression.
Functional heads do not satisfy this semantic requirement. For this reason, they are often optional
leading to the need to posit lots of headless constructions. For example,
the nominals John, books, and
rice all lack a determiner
and must have an empty head if functional heads are assumed
(cf. Carnie, 2011).
On the other hand, *the, and *a are ungrammatical despite having
a "functional head" (scare quotes intended) since they lack sufficient semantic content to function
as referring expressions.
Even more telling, pronouns like he and she
normally do not allow a determiner (e.g. *the he,
making the claim that there is an empty determiner entirely suspect and forcing some linguists to conclude
that pronouns are really pro-determiners
(cf. Cooke & Newsome,1997)!
Comparing the specifier and head functions in Double-R's instantiation of these functions,
the specifier is far more likely to be optional.
This is because the specifier, which primarily functions to establish reference via
the definiteness grammatical feature in the case of nominals,
is not the only source of definiteness, which is often jointly encoded by the lexical head.
Proper nouns, plural nouns, mass nouns and pronouns (not pro-determiners) functioning as heads are all capable of
projecting the definiteness grammatical
feature which is
otherwise the primary contribution of the specifier. Interestingly, singular count nouns appear not to project a
definiteness feature (e.g. *book is good) and require separate specification in normal English
(e.g. the book is good)
(Ball, revised 2013).
Returning to the example, the word what is categorized as a wh-nominal-det that functions as the
head of a wh object referring expression.
The wh object referring expression functions as the wh-focus
of the wh-question construction.
The wh-focus is a grammatical function that is specific to wh-questions.
From an incremental processing perspective, it is important to retain access to the wh-focus
beyond its initial processing to support
the resolution of long-distance dependencies. In the example,
the wh-focus what also functions as the implict object of given
(and the implict subject and implicit object of to be eaten).
In Double-R, access to the wh-focus is supported by retaining it in a special wh-focus buffer.
At the incremental processing of given, the wh-focus is available in the wh-focus buffer
to be bound by the implicit object of given
(the object is predicted by the ditransitive verb construction associated with given).
The use of buffers to provide long-distance access to grammatical functions like the wh-focus
is the Double-R equivalent of movement (or copying) in generative grammar.
When the implicit object of given is bound to the wh-focus, the wh-focus is copied into
an object buffer to support subsequent processing. At this point in processing,
the same linguistic element (the wh object referring expression headed by what)
is accessible in two buffers which reflect its dual grammatical functions.
It is the accessibility of what in the object buffer which supports the binding of
the implied subject and object of to be eaten to it. The subject and object of to be eaten are
predicted by the transitive verb construction associated with eaten.
The binding of the implied object of to be eaten to the implied subject is supported by
the passive construction which is cued by the -en form of eaten which projects a passive voice feature.
Ultimately, the wh-word what winds up filling four distinct grammatical functions:
To support the processing of embedded clauses, Double-R maintains separate matrix and embedded clause buffers
for the subject, object and indirect object (e.g. subject buffer vs. embedded subject buffer;
object buffer vs. embedded object buffer).
- wh-focus of wh-question headed by given
- implicit object of given
- implicit subject of to be eaten
- implicit object of to be eaten
There are three kinds of implicit argument in Double-R:
The terms PRO and trace are adapted from generative grammar
(Chomsky, 1987) and empty (e) comes from the grammar of Huddleston and Pullum.
PRO corresponds to the implicit subject
of a tenseless clause. The implicit subject of an infinitive situation referring expression like to be eaten is a PRO.
If there is another referring expression in the input that is co-referential with
the implicit subject, then the implicit PRO subject is bound to that referring expression via
the bind index (bind-indx).
Note that the bind index of the wh-focus what and the PRO subject of to be eaten
are the same (5), indicating co-reference.
This binding is actually indirect in this example.
The trace object of given is bound to the wh-focus what.
The PRO subject of to be eaten
is subsequently bound to the trace object of given.
Trace corresponds to a displaced argument
— i.e. a predicted argument of a relation that does not occur in canonical position.
If there is another referring expression in the input that is co-referential
with the implicit argument, then the trace is bound to that referring expression. The distinction
between PRO and trace comes from the generative grammar view that a trace involves movement.
The constituent that is moved leaves behind a trace. While there is no actual movement in Double-R,
there are focus constructions which behave very much as though movement is involved (based on
accessibility in buffers).
is a focus construction with a special wh-focus function that does not occur in a normal declarative
construction. Whenever there is an expression filling a focus function in a construction, there will be a corresponding
grammatical function without an explicit expression. This correspondence is typically one of co-reference.
Empty (e) subject arguments occur in tensed imperative clauses. Unlike PRO and trace which typically refer
via binding to an existing referring expression (i.e. via co-reference),
empty (e) arguments refer deictically like the pronouns you and we.
- empty (e)
The word could has the grammatical type of an auxiliary verb (auxiliary)
(subcategorized as a modal auxiliary, but not shown in the diagram)
and functions as an operator within the wh-question construction.
The operator function is specific to a limited number of constructions including wh-questions and yes-no-questions.
The operator function comes from the grammar of Quirk et. al (1972, 1985) and is adopted in Double-R.
Could also has the grammatical features tense-1 = finite (fin),
tense = present (pres), and
modality = could. These features project to the clause.
The word he has the grammatical type of a personal pronoun (pers-pron),
another subtype of pronoun,
and functions as the head of a pronoun object referring expression
(pron-obj-refer-expr). The grammatical function head was important in the analysis
of Chomksy (1970) which introduced X-Bar Theory.
Double-R adopts this grammatical function and aspects of X-Bar Theory (Ball, 2007a),
but differs from Chomsky (1970) in explicitly representing the head function,
rather than assuming a configurational identification.
This pronoun object referring expression functions as the subject (subj) of the wh-question construction.
The multi-word unit have been has the grammatical type of a double word auxiliary verb (aux+aux)
and functions as the specifier (spec) of the wh-question construction.
The specifier function also originates in Chomsky (1970) where it was applied to auxiliary verbs and determiners.
Double-R adopts this grammatical function and the analysis of specifiers in Chomsky (1970)
— which has since changed,
but differs from Chomsky (1970) in representing the specifier function explicitly,
rather than configurationally. Have been also projects the features aspect = perfect (perf) (from have) and
voice = inactive (inact) (from been) to the clause.
The word given has the grammatical type of a verb and functions as the head of
a predicate ditransitive verb construction, which in turn functions as the head of the wh-question construction.
Given also has the grammatical features voice = passive (pass) and
aspect = perfect (perf).
These features project to the clause. The passive voice feature of given overrides the inactive (inact)
feature of been. The perfect aspect feature of have is also overriden by the perfect aspect feature of given.
The result is that no grammatical features from have been survive. However, have been still has a
grammatical function since given cannot occur after could which must be followed by a base form verb
(e.g. he could give vs. *He could given).
Been is also needed since given following
have is not passive, but active voice (e.g. I have given is active voice despite given).
The active voice feature of have blocks
the passive voice feature of given from projecting in I have given.
As a special case, been has the effect of neutralizing
the active voice feature of have effectively making it inactive (only been can do this),
so that the passive voice feature of given can project (i.e. the passive voice feature of given
overrides the inactive voice feature of been).
The projection of verb features is considered in detail in Ball (2012) and in the
verbal features section of this document.
The multi-word unit to be is the infinitive verb form of be. It functions as the specifier of the
embedded clause headed by eaten. To be encodes the grammatical features tense-1 = non-finite (non-fin),
tense = none (not shown in the diagram), and voice = inactive (inact)
(also not shown since it is overriden).
The word eaten has the grammatical type of a verb and functions as the head of predicate transitive verb
construction, which functions as the head of an embedded clause,
which functions as the clausal complement (comp) of a predicate ditransitive verb construction.
Most predicates optionally take a clausal complement, typically an infinitive clause. In this case,
the clausal complement is also a passive construction due to the passive form eaten which functions as
the head and projects the feature voice = passive (pass).
Eaten also projects the feature aspect = perfect (perf) to the clause.
Passive constructions differ from focus constructions in that although there is a trace expression,
there is no specialized focus function.
Instead, the subject grammatical function is used to realize the trace expression. To represent this,
Double-R binds the trace expression, either the trace object or the trace indirect object, to the subject.
Double-R also projects a voice grammatical feature with the value passive (pass) to indicate the passive construction.
Although the passive voice feature is projected from past-participle verb forms (given and eaten),
it is only displayed at the level of the clause in the diagrams.
In sum, trace expressions are licensed by constructions like wh-question and passive which suggest movement,
especially when a focus function is involved.
PRO expressions are licensed by constructions like infinitives which do not suggest movement,
but where the subject function is implicit. Trace and PRO expressions get their grammatical features
indirectly from the referring expressions to which they bind.
There is a third category of implicit argument called empty (e).
In Give me it!, the implicit subject of give is categorized as e,
with the grammatical features person = second,
number = plural and animacy = human,
which identify the implicit subject as equivalent to the pronoun you.
In Let's go!,
the implicit subject is also categorized as e with the grammatical features person = first,
number = plural and animacy = human,
which identify the implicit subject as equivalent to the pronoun we.
In this subsection we step through the incremental processing of the example sentence
What could he have been given to be eaten?
The processing of the word what leads to projection of a wh object referring expression (wh-obj-refer-expr)
that is placed in the wh-focus buffer. The processing of what does not lead to projection of
a wh-question construction because what alone does not distinguish between wh-questions
and wh situation referring expressions (wh-sit-refer-expr) like
what he wanted was a lollipop.
Buffers — Double-R uses language specific buffers to retain the products of language analysis
to support subsequent processing. A description and full list of buffers is available in the
buffers section of this document.
- wh-focus ← wh-obj-refer-expr with what as head
what could →
The processing of could following what provides sufficient context to project a wh question situation referring expression (wh-quest-sit-refer-expr).
In this construction, the wh-obj-refer-expr projected by what and stored in the wh-focus
buffer is integrated as the wh-focus.
An implicit PRO subject is created and bound to the wh-focus via
the bind index (bind-indx) slot with matching value 5.
The wh-focus is copied
into the subject buffer, but also remains in the wh-focus buffer.
- situation ← wh-question-sit-refer-expr with wh-focus, subject and specifier
- wh-focus = wh-obj-refer-expr with what as head
- subject ← wh-obj-refer-expr with what as head
- specifier ← auxiliary could
what could he →
The processing of he in the context of what could causes the implicit PRO subject
to be replaced by the pronoun object referring expression (pron-obj-refer-expr)
projected by he. This pron-obj-refer-expr overrides the wh-obj-refer-expr that was in
the subject buffer. The auxiliary could is moved from the specifier function to
the operator function and placed in the operator buffer. The specifier buffer is emptied.
- situation = wh-question
- wh-focus = wh-obj-refer-expr with what as head
- operator ← auxiliary could
- subject ← pron-obj-refer-expr with he as head
- specifier ← "nothing"
what could he have been →
The processing of have been leads to its recognition as a multi-word auxiliary (aux+aux) that is integrated
as the specifier
of the wh-quest-sit-refer-expr construction. In addition, a trace pron-obj-refer-expr
is created and bound to the wh-focus. This demonstrates the greedy nature of the mechanism
for resolving long-distance dependencies involving focus elements. Note
that What could he have been? is a complete question with what functioning as the implicit trace head.
The wh-focus is copied into the
predicate buffer where the head of the wh-quest-sit-refer-expr that is in the situation buffer
- situation = wh-question
- wh-focus = wh-obj-refer-expr with what as head
- operator ← auxiliary could
- subject = pron-obj-refer-expr with he as head
- specifier ← aux+aux have been
- predicate ← wh-obj-refer-expr with what as head
what could he have been given →
The processing of given leads to projection of a predicate ditransitive verb (pred-ditrans-verb)
construction that replaces the trace pron-obj-refer-expr
as the head of the wh-question. The pred-ditrans-verb is placed in the predicate
buffer. Because this is a passive construction, a trace pron-obj-refer-expr
is created and integrated as the indirect object (iobj). The trace indirect object is bound to the subject.
The subject is copied into the indirect object buffer.
Because this is a wh-focus construction,
a trace pron-obj-refer-expr
is created and integrated as the object (obj). The trace object is bound to the wh-focus.
The wh-focus is copied into the object buffer.
The decision to bind the trace indirect object to the subject and the trace object
to the wh-focus results from the human feature of the subject he, and the
inanimate feature of the wh-focus what.
- situation = wh-question
- wh-focus = wh-obj-refer-expr with what as head
- operator = auxiliary could
- subject = pron-obj-refer-expr with he as head
- specifier = aux+aux have been
- predicate ← pred-ditrans-verb with given as head
- indirect object ← pron-obj-refer-expr with he as head
- object ← wh-obj-refer-expr with what as head
what could he have been given to be →
The processing of to be leads to projection of an infinitive situation referring expression (inf-sit-refer-expr)
with the auxiliary to be functioning as the specifier
and an expectation for a head indicated by the slot value head-indx.
In the context of a matrix predicate with
an available (clausal) complement (comp) slot, the
inf-sit-refer-expr is integrated as the complement. An implicit PRO subject is created and
bound to the indirect object of the matrix predicate. This is the default preference since
indirect objects are often human or animate. The pron-obj-refer-expr with head he (in the matrix
subject buffer) is copied into the embedded subject
buffer which is distinct from the matrix subject buffer. The inf-sit-refer-expr is placed in the
embedded situation buffer which is also distinct from the matrix situation buffer. Since
there is only one specifier buffer, this buffer must be reused to process the embedded clause.
- Matrix Clause
- situation = wh-question
- wh-focus = wh-obj-refer-expr with what as head
- operator = auxiliary could
- subject = pron-obj-refer-expr with he as head
- predicate = pred-ditrans-verb with given as head
- indirect object = pron-obj-refer-expr with he as head
- object = wh-obj-refer-expr with what as head
- Embedded Clause
- embedded situation ← inf-sit-refer-expr with specifier to be
- embedded subject ← pron-obj-refer-expr with he as head
- specifier ← auxiliary to be
what could he have been given to be eaten →
The processing of eaten leads to projection of a predicate transitive verb construction
which is integrated as the head of the inf-sit-refer-expr in the embedded situation buffer.
Since this is also a
wh-focus construction (i.e. the wh-focus crosses clause boundaries), a trace object is projected and bound to the wh-focus.
The wh-focus is copied into the embedded object buffer.
In addition, since this is a passive construction, the implied PRO subject is bound
to the embedded object which is now the wh-focus. Finally, the embedded object
is copied into the embedded subject buffer.
- Matrix Clause
- situation = wh-question
- wh-focus = wh-obj-refer-expr with what as head
- operator = auxiliary could
- subject = pron-obj-refer-expr with he as head
- predicate = pred-ditrans-verb with given as head
- indirect object = pron-obj-refer-expr with he as head
- object = wh-obj-refer-expr with what as head
- Embedded Clause
- embedded situation = inf-sit-refer-expr with specifier to be and head eaten
- embedded subject ← wh-obj-refer-expr with what as head
- specifier = auxiliary to be
- embedded predicate ← pred-trans-verb with eaten as head
- embedded object ← wh-obj-refer-expr with what as head
What could he have been given to be eaten? →
The processing of ? confirms the earlier decision to project a wh-question construction.
In Double-R, punctuation typically guides processing, but doesn't otherwise appear in representations.
However, it is possible for sentence final punctuation to alter the discourse function of a
sentence. When this happens, the matrix clause level discourse function (df) slot is provided
to indicate this. However, discourse function is better represented at the level of discourse.
Unfortunately, Double-R currently has only the beginnings of a capability to provide discourse
Incremental Processing Summary
We have now finished the incremental processing of the example sentence. At each step in processing, Double-R
builds the best representation it can given the current input and preceding context. However, we have
seen numerous places where the locally best choice had to be adjusted given the
subsequent input. Such is the nature of language analysis in Double-R, and presumably in
Human Language Processing, as well. Most of the examples in the rest of this document
discuss the processing of simpler inputs, building up to the kind of integrated processing required
to handle this extended example.
Chapter 4: Sub-Lexical Representation and Processing
Phonetic, Phonological and Syllabic Representation and Processing
The focus of the computational implementation of Double-R is on the processing of written text.
For this reason, the representation and processing of phonetic, phonological and syllabic knowledge
is absent. However, it is clear that phonetic, phonological and syllabic knowledge does contribute
to the processing of written text. This is especially true in the processing of text chat
which makes extensive use of sound correspondences to shorten messages as in
A more complete system would incorporate phonetic, phonological and syllabic knowledge even for the analysis
of written text.
At this point, the best defense that can be made of Double-R is that it is amenable to the
addition of phonetic, phonological and syllabic knowledge. This stems from the interactive and incremental nature of
the language analysis capabilities in Double-R. Once added, phonetic, phonological and syllabic knowledge will
interact with knowledge at other levels of linguistic representation to improve the behavior of Double-R.
Orthographic Representation and Processing
As a computational compromise, Double-R only encodes knowledge about the letters and trigrams which make up the words
in the mental lexicon. The letters and trigrams have been computed automatically.
Other forms of orthographic representation that have been proposed on the basis of psycholinguistic analysis
(e.g. syllables, roots, stems, BOSS) are not used because of the challenges of computing them automatically.
We would especially like to see a representation of syllables as sequences of letters in the mental lexicon
and plan to include them once a suitable resource is identified.
Morphological Representation and Processing
Double-R currently has only rudimentary morphological representation and processing capabilities.
The lack of morphological representation and processing capabilities is compensated for to large extent
by the inclusion of the morphological variants of words as separate entries in the mental lexicon.
The reasons for including morphological variants in the mental lexicon are primarily due to processing
and usage considerations.
Processing is more efficient and less error prone when morphological variants are included. If morphological
variants are not included in the mental lexicon, then there is no direct match from the physical form of the
input to the representation in the mental lexicon. For example, consider the input
John is reading all the books
The words is, reading and books are highlighted because they are morphological variants that are
derivable from the base forms be, read and book. If is, reading and books
are not represented in the mental lexicon, then it will be necessary to first determine that the
form is matches the base form be in the mental lexicon,
reading matches the base form read in the mental lexicon, and books matches the
base form book in the mental lexicon. Once this is done and assuming it is successful given the mismatch in form
(especially in the case of is and be which are completely different in form),
the word is and the endings -ing and -s must be separately processed to determine which
is associated with the base form retrieved from the mental lexicon. In general, this extra processing
is undesirable. Instead, if the morphological variants is, reading and books are represented
in the mental lexicon, then a direct match between the form of the input and the form in the mental lexicon
will be available and will facilitate processing.
Since humans are exposed to morphological variants and not base forms in linguistic inputs, a usage based
account suggests that they will be represented as such in the mental lexicon. Determining the base form
is a morphological process that occurs on the basis of morphological knowledge given morphological variants.
Once these morphological regularities are learned, they can be used to analyze previously unseen inputs.
But for inputs that have been experienced, there is no good reason for them not to be encoded in the mental lexicon.
To argue otherwise, would be to commit what Langacker (1987) calls the Rule/List Fallacy
The rule/list fallacy is the assumption, on grounds of simplicity, that particular statements (i.e. lists)
must be excised from the grammar of a language if general statements (i.e. rules) that subsume
them can be established.
A grammar which excludes lists where rules are available might be considered to be more elegant, but
from a processing perspective, it will be
far less efficient
— especially given the large capacity for storing knowledge in the human brain and
the efficient retrieval mechanisms that are available for accessing that knowledge.
Of course, Double-R has the opposite problem. It largely lacks a representation of morphological rules
which are needed to support the processing of novel words which aren't available in the mental lexicon.
Nonce expressions like the newspaper boy porched the newspaper (Clark & Clark, 1979), where porched means
threw the newspaper on the porch are a classic example.
A morphological analysis capability that recognizes -ed as the
past tense verb form would be very helpful for making sense of this novel use of porch.
Chapter 8: The Situation Model and the Mental Universe
Double-R Grammar representations identify the referring expresions in the linguistic input and
and the relationships between those referring expressions. These grammatical representations capture aspects
of referential and relational meaning. However, they are composed of words and
are not full representations of meaning since they are symbolic.
For a fuller representation of meaning, the referring expressions need to be associated with
the objects and situations to which they refer. We call this association one of grounding
in the sense of Harnad (1999) and his symbol grounding hypothesis.
The objects and situations to which referring expressions refer are themselves conceptual representations
in the spirit of Jackendoff (2002).
There is no direct reference to the external world. When we encounter the world, we build up
a representation of that world which is referred to as a Situation Model (van Dijk & Kintsch, 1983).
Grammatical representations tap into and can help create the situation model
as does our mental universe which contains the knowledge of the world acquired over a lifetime
of experience. The mental universe provides the context
for creation of a situation model that goes beyond the current situation and linguistic input.
Despite the availability of linguistic input, situation model
representations are conceptual, not linguistic. Where we differ from Jackendoff is in our unease in
using uppercase words as stand-ins for conceptual representations. We refer to the use of uppercase words
for concepts as uppercase word syndrome. Whatever conceptual representations are, they are not
uppercase words. However, lacking an adequate representational system for concepts, we currently do no
better than Jackendoff. Our situation model representations contain word indices which generalize over
morphological variants (e.g. book-indx for book and books) and we drop the uppercase pretense.
When we have a better theory of how the human experience of objects and situations results in
conceptual representations of those objects and situations, we will be on a firmer footing for
grounding Double-R representations and providing a fuller representation of meaning.
For now, there are a few assumptions we try to adhere to, including:
We follow Hobbs (2003) in attempting to minimize the mapping from linguistic to conceptual representation.
We also follow Hobbs (1985) in taking an ontologically promiscuous approach to semantic representation
and eschew logical approaches like First Order Predicate Calculus which minimize
the number of ontological categories. First order logic
may be fine as a logical formalism, but it is inadequate to represent the range of meanings
conveyed in natural language. If anything, conceptual representations will be more complex
than grammatical representations.
- Mimimize the mapping from linguistic representation to conceptual representation (Ball, 2010c)
- Avoid use of ontologically inadequate formalisms
- Don't put things in grammatical representations which belong in conceptual representations
Double-R grammatical representations lack semantic roles like agent and patient.
We assume instead, that these roles are captured in the mapping from grammatical to conceptual
representation. The referring expression functioning as the subject of an active sentence with
a verbal predicate typically maps
to the object that functions as the agent of the action in the situation model representation.
The referring expression functioning as subject of a passive sentence with a transitive verbal predicate
typically maps to affected object in the situation model representation.
While the details of this mapping are not yet worked out in Double-R, the alternative of assigning
semantic roles to referring expressions in grammatical representations is not supported by
the grammatical evidence. There is considerable grammatical evidence for functions like subject and object,
but little grammatical evidence for the grammatical encoding of semantic roles like agent and patient.
Other than stating these assumptions, we will have little to say about situation model representations.
To be fair to ourselves, it is not clear that a theory of conceptual representation belongs
in a grammatical description. For the purposes of this document, the conceptual system is
essentially a black box, as it is and has always been in Chomsky's theories. With respect to
generative grammar, we view Double-R representations as similar to LF representations,
except that quantification is represented grammatically, not logically, in Double-R,
and Double-R representations are semantically motivated and not purely syntactic.
With respect to Jackendoff's
conceptual representations, we view them as comparable to Double-R grammatical representations
consisting of word indices, rather than concepts.
Caveats aside, we have actually implemented a domain specific situation model in our
synthetic teammate project (Rodgers et al., 2012),
and the results of that effort will inform future research aimed at creating a more general
situation modeling capability as well as a representation of the mental universe.
From Referring Expression to Referent
In Jackendoff’s Conceptual Semantics (Jackendoff, 1983,
1990, 2002, 2007), reference to places, directions, times,
manners, and measures in addition to situations and objects
is supported, but reference is limited to tokens or instances
of these conceptual categories, adhering to the basic notion
that reference is to individuals. We propose an
extension of Jackendoff’s referential types along an
orthogonal dimension of reference which is cognitively
motivated in suggesting the possibility of referring to types,
prototypes and exemplars in addition to instances.
Reference to classes and collections of referential types and
vacuous instances and collections is also considered.
The primary motivation for expanding the ontology of
referential types is to simplify the mapping from referring
expressions to corresponding representations of referential
meaning. Hobbs (2003) pursues a similar strategy in
arguing for logical representations that are as close to
English as possible. Jackendoff’s (1983, p. 13-14)
grammatical constraint makes a related claim:
…one should prefer a semantic theory that explains
otherwise arbitrary generalizations about the syntax
and the lexicon…a theory’s deviations from efficient
encoding must be vigorously justified, for what
appears to be an irregular relationship between
syntax and semantics may turn out merely to be a
bad theory of one or the other (italics added)
Taking the grammatical constraint seriously, we assume
that if a linguistic expression has the grammatical form of a
referring expression, then it is a referring expression. For
example, a nominal like a man which contains the
referential marker a, indicates that the expression can be
used to refer. Unless there is a very strong reason to assume
that any use of this referring expression is non-referential, it
is assumed to refer. Further, the referential marker a
indicates reference to a single referent as does the head
noun man (i.e. both are grammatically singular). This
expression cannot be used to refer to multiple individuals
under normal circumstances.
Where other approaches argue for the non-referential use
of referring expressions or for a complicated mapping from
referring expression to possible referents (see discussion
below), it is argued instead that referring expressions may
refer to something other than an individual, and that the
notion of reference is complicated by a secondary
relationship between the referents in a situation model and
objects in the mental universe. By expanding the ontology
of referential types to include types, prototypes and
exemplars, and classes and collections of these, it is
possible to retain a simplified mapping from referring
expression to referent
— one which is consistent with the
grammatical features of the referring expression. By
introducing a bi-partite relationship between a situation
model and the mental universe, it is possible to explain
apparent non-referential uses of referring expressions. The
viability of this approach hinges on adoption of the
mentalist semantics of Jackendoff. Reference is to mental
encodings of external experience and these encodings can
provide alternative construals of reality. There is no direct
reference to actual objects in the external world.
Ball (2007) presents a linguistic theory of the grammatical
encoding of referential and relational meaning which is
implemented in a computational cognitive model of
language comprehension (Ball, Heiberg & Silber, 2007;
Ball et al., 2010) within the ACT-R cognitive architecture
(Anderson, 2007). The basic structure and function of
nominals and clauses is bi-polar with a specifier functioning
as the locus of the referential pole and a head functioning
as the locus of the relational pole
— where relational pole
encompasses objects (noun, proper noun, pronoun) and
relations (verb, adjective, preposition, adverb). If the head
of the relational pole is a relation, one or more complements
or arguments may be associated with the relation. Modifiers
may surround the specifier and head and may be
preferentially attracted to one pole or the other. A specifier
and head combine to
form a referring expression. A determiner functioning as an
object specifier combines with a head to form an object
referring expression or nominal (ORE → Obj-Spec Obj-Head).
A possessive nominal (e.g. John’s in John’s
book) or possessive pronoun (e.g. his in his book)
functioning as specifier and called a reference point by Taylor (2000)
may also combine with a head to form an object referring
expression. In this
case the object referring expression contains two referring
Ball (2010; revised 2013) extends the theory of referential and
relational meaning to a consideration of grammatical
features like definiteness, number, animacy, gender and
case in object referring expressions. These features provide
important grammatical cues for determining the referents of
object referring expressions.
The referring expressions in a text instantiate and refer to
objects, situations, locations, etc. in a situation model which
is a representation of the evolving meaning of the text. The
term situation model originates in the research of van
Dijk & Kintsch (1983). Originally a situation model was
viewed as a collection of propositions extracted from a text
and elaborated with additional propositions introduced by
schemas activated by the text and resulting from inference
processes operating over the text. However, situation
models have evolved away from being purely propositional
(or relational) representations towards encoding referential,
spatial, imaginal and even motor aspects of meaning (cf.
Zwann and Radvansky 1998). We view the situation model
as the cognitive locus of Jackendoff’s Conceptual
Semantics. Jackendoff has adopted similar extensions in his
recent work (Jackendoff, 2002, 2007).
- the reference point functioning as specifier
- the referring expression as a whole
A situation model is a mental scratchpad for maintaining
information about the referents of the referring expressions
in a text. However, referents can also be implicit in the text,
inferred from background knowledge or encoded from the
environment. The situation model is constructed in the
context of a mental universe. The mental universe is the
experience of the real world filtered through the perceptual
and cognitive apparatus of an individual over the course of
a lifetime. Like situation models, the mental universe may
be full of counterfactual objects and situations. An
individual may have a long history of experience of
unicorns, both perceptual (e.g. from movies and picture
books) and linguistic, despite the fact that unicorns only
exist as figments of imagination in objective reality. The
mental universe may also have well established and distinct
referents for the morning star and the evening star, despite
the fact that these referents map to the same planet in
The combination of the mental universe and the situation
model provide the basic sources for grounding the meaning
of referring expressions. A referring expression may be
bound to a referent in the situation model which may or
may not be ground in the mental universe. If the referent is
ground in the mental universe then the individual has
personal experience of the referent. If the referent is not
ground in the mental universe, then the individual has only
limited information about the referent and it may appear
that the referring expression is non-referential. But as
Lyons (1977) notes, allowing referring expressions to be
non-referential is problematic for co-reference. Two
expressions cannot have the same reference, if one of them
is not a referring expression at all (Ibid, 191). In John’s
murderer, whoever he is…, he co-refers with John’s
murderer. The attributive use of a referring expression like
John’s murderer is a type of reference which instantiates
a referent into the situation model that is not grounded in
the mental universe, but which supports co-reference.
The ontology of referential types presented in this document
follows from basic principles of Cognitive Linguistics (cf.
Langacker, 1987; Lakoff, 1987) and Cognitive Psychology
(Rosch, 1975; Collins and Quillian, 1969). There is
extensive empirical evidence supporting the existence of
conceptual categories corresponding to types, prototypes
and exemplars. We take the small step of
suggesting that such conceptual categories can be referred
to by linguistic expressions and explore the consequences.
The representation of referents in the situation model
parallels the representation of referring expressions. Both
are represented in ACT-R as chunks
— i.e. frames with
collections of slot-value pairs. Chunks are organized into an
inheritance hierarchy which supports default inheritance
and a distinction between chunk type and chunk instance.
The value of a slot may be a chunk, supporting complex
representations of structure needed for linguistic and
Conceptual Semantic representation. With respect to object
referring expressions which are the focus of this section, a
chunk representing an object referring expression is bound
to a corresponding referent via a matching value in an index
slot. Depending on the object referring expression, situation
model and mental universe, the referent may be an instance,
type, prototype, exemplar, class or collection.
An Expanded Ontology of Referential Types
First Order Predicate Calculus (FOPC) is typically
grounded in a model theoretic semantics with an ontology
limited to atomic individuals. The model consists of a
domain and a set of individuals in that domain and nothing
else. Typically these individuals are assumed to correspond
to objects (or individuals) in the real world being modeled.
In FOPC, a relation is modeled in terms of the set of
individuals (for 1-ary relations or properties) or set of
ordered sets of individuals (for n-ary relations, n > 1) for
which the relation is true. A relation with its arguments
bound to individuals in the domain is either true or false of
those individuals and it is said that the reference of the
proposition is one of the values true or false.
Situation Semantics (Barwise and Perry, 1983) extends
FOPC by allowing situations to be individuals. Not only are
situations true or false of sets of individuals in the domain
being modeled, but they are themselves individuals in the
domain. We may say that situations have first-class status
in situation semantics, whereas they are a second-order (or
derived) notion in standard FOPC.
Situation Semantics is a step in the right direction.
Whereas it might make reasonable sense to suggest that a
predicate like dog denotes the set (or class) of individuals
that are dogs (although psychologically humans cannot
quantify over such a large set), it makes little sense to
suggest that the predicate run denotes the set of all
individuals who run, or that kick denotes the set of
ordered sets of kickers and kickees, as is typical in FOPC
treatments with a set-theoretic model limited to individuals
that are essentially objects of various types (and sets of such
individuals). (It is this sleight of hand in FOPC that
collapses the distinction between nouns and verbs, treating
both as predicates corresponding to sets of individuals.) It is
much more reasonable to suggest that run denotes the set
of all running events and that kick denotes the set of all
kicking events. And if run denotes a set of running events
and kick a set of kicking events, then allowing run to
be used in an expression that refers to an instance of a
running event, and allowing kick to be used in an
expression that refers to an instance of a kicking event,
follows quite naturally and is cognitively plausible.
However, Situation Semantics stops short. What is needed
is a referential ontology which supports a mapping from the
types of referring expressions which are linguistically
attested to the types of referents which are cognitively
With an ontology of referential types limited to
individuals and sets of individuals, it is often assumed that a
referring expression like a car in an expression like a car
is a vehicle quantifies over the set of all individuals for
which the predicate car is true (i.e. the set or class of
objects of type car). In FOPC, this can be represented as
∀x (car(x) → vehicle (x))
However, from a grammatical perspective, a car is clearly
singular, and from a cognitive perspective, quantifying over
all individuals is cognitively implausible. The need to
quantify over all individuals in the FOPC representation of
the linguistic expression stems from the limited ontology
available in FOPC for representing the meaning of
indefinite referring expressions. Only the universal and
— which fail to capture the full range
of quantification in natural language
— are available.
Similarly, one FOPC representation for the expression
every man owns a car is given by
∀x (∃y (man(x) and car(y) → own(x,y)))
However, in English every man is grammatically
singular, and a mapping to the universal quantifier is
problematic. Johnson-Laird (1983) introduced mental
models as a way of overcoming the limitations of
quantification in FOPC (among other things). He suggests
that the expression a car in the sentence every man owns
a car maps to some representative subset of cars. This
representative subset of cars corresponds to the
representative subset of individuals referred to by every
man, plus a subset of cars that are not owned. He (1983, p.
421) represents this as
man → car
But if every man and a car are singular and not plural,
then every man does not refer to multiple men and a
car does not refer to multiple cars. Johnson-Laird’s
treatment is cognitively plausible, but inconsistent with the
grammatical form of the referring expressions. From a
perspective which assumes that the number feature of a
referring expression corresponds closely to the number
feature of the referent of the expression, there are several
cognitively motivated referents for expressions like every
man and a car which do not violate the singular status of
the linguistic expressions:
man → car
A car may refer to a type of object, namely the type of
object that is a car. A car may also refer to a prototype
that represents what is common to most cars, or it may refer
to an exemplar which is an instance that is a representative
car. Further, a car may refer to an indefinite instance with
the determiner a marking the indefinite status of the
referent of a car. Note that indefinite instance is used
here as a referential type and not a type of referring
expression. In all but a few cases, the type of the referring
expression is an indefinite, singular object referring
expression when grammatically marked by the determiner
a and a singular head noun (a few cases being a notable
exception where a combines with a plural head noun).
Given the occurrence of the indefinite, singular determiner
a and the singular noun car in this expression, a car
cannot be used to refer to a definite instance of a car, or to a
class or collection, but all the other referential types are
potential referents of indefinite, singular object referring
expressions. Likewise, every man may refer to a
representative but indefinite, singular instance of a man as
is suggested by the singular status of every man.
- Indefinite/Definite Instance
Reference to Definite and Indefinite Instances
determiner the marks reference to definite instances.
Consider the definite object referring expression the car.
This definite expression indicates that there is already a
referent in the situation model that is being referred to or
that there is a salient car object in the mental universe
that is being referred to and this object should be
instantiated into the situation model. For a more complex
A car is in the driveway. The car is red.
In the first sentence, the expression a car is indefinite and
instantiates a new referent into the situation model
that is not (known to be) ground in the mental universe. In
the second sentence, the expression the car is definite and
refers to the referent instantiated into the situation model by
a car. Note that this referent is ungrounded in the sense
that it has not been identified with any object in the mental
universe, although it could be (e.g. Oh, it’s your car). It is
the mental universe which ultimately grounds referents. In
the first sentence, the expression the driveway is definite.
In this case, the definiteness of the driveway indicates
there is (or should be) a salient object in the mental
universe that should be instantiated into the situation model.
There are three primary types of definite reference:
There are two primary types of
reference to an existing referent in the situation model
which is grounded in the mental universe
- reference to an
existing referent in the situation model which is ungrounded
in the mental universe
- reference to a object in the
mental universe which is not in the situation model, but is
(or should be) salient
- reference to an object which is
being introduced and should be instantiated into the
— this object is not known to correspond to
any object in the mental universe
- reference to a
generic instance or type which exists in the mental universe
and should be instantiated into the situation model
Reference to Types
Type hierarchies are common in
systems of knowledge representation and making types first
class objects allows expressions like a sedan is a (type of)
car or a (type of) car I like is a sedan to be represented
as relating two types a sedan and a car. A sedan and
a car refer to instances of a type. The suggested reference
to a type rather than a class of instances is based on the
singular status of these referring expressions (i.e. a sedan
vs. all sedans). A type is a reified class. From a referential
perspective, the type is atomic with no subparts and
singular reference is appropriate. An instance is added to
the situation model which is grounded in a type in the
mental universe. From a relational perspective, is
establishes a relationship of equality between the two
arguments a sedan and a car. However, from a
referential perspective, there are two basic possibilities:
The typical treatment of
predicate nominals suggests that they are non-referential
(cf. Jackendoff, 2002). In a sentence like John is a fool,
is a fool is treated as a predicate nominal that says
something about the individual that John refers to and
this sentence is often considered synonymous with John is
foolish. From the perspective of the grammatical
constraint, there is a problem with this treatment.
Grammatically, a fool has the form of an indefinite,
singular object referring expression and all object referring
expressions are capable of referring, regardless of context.
In the case of a predicate nominal, the referent of the
embedded object referring expression, if it is identified, is
the same as the referent of the subject
— they are coreferential.
The assumption that is a fool is nonreferential
rests on the availability of a referring expression
John, the referent of which the predicate nominal is a
fool is predicated. In the absence of a separate referring
expression, it is unclear how to treat the predicate nominal.
For example, in I wonder who is a fool, if who is nonreferential
as Huddleston & Pullum (2002, p. 401) suggest,
then what does is a fool get predicated of? An obvious
suggestion is that who functions as an unbound variable
(or variable bound via a lambda expression) which
instantiates a referent whose grounding is yet to be
determined, but which supports predication of is a fool
and can be referred to subsequently as in the follow up he
better be careful. In fact, it may turn out that nobody is a
fool since wonder is non-factive (i.e. doesn’t entail the
existence of its complement). Or it may be the case that the
hearer can provide the grounding as in It’s John. In
general, Huddleston & Pullum discuss a range of nonreferential
object referring expressions (they prefer to use
the term NP) in which there is no object in the real world to
which the expressions refer, overlooking the possibility of a
more flexible notion of reference within a situation model
embedded in a mental universe.
- both a sedan and a car may refer to types of objects
which are equated
- the occurrence of a car within
the context of is suppresses the normal referential
behavior of a car such that is a car
— a predicate nominal
— is treated as a non-referential expression which is
ascribed to the subject a sedan
In Jackendoff (2002), types are treated as lacking an
indexical feature. While this treatment is attractive in
providing a simple distinction between types and tokens
(i.e. tokens have an indexical feature, types don’t), the lack
of an indexical feature implies an inability to refer to types.
Yet, Jackendoff acknowledges the existence of NPs which
describe types. These NPs are necessarily non-referential.
When an NP occurs as a predicate nominal and functions as
a kind (or type) as in a professor in John is a professor,
this approach coheres. There is an object in the situation
model to which the expression refers. But what happens
when an NP describing a type occurs as the subject or
object as in A new kind of car is passing by or He wants
a special kind of dog? If the object referring expressions
don’t refer, then it is unclear how the situation model can
represent the meaning of these expressions. At a minimum,
Jackendoff needs to allow reference to generic instances
and argue that apparent references to types are really
generic instance references. However, since there is strong
evidence that types exist as mental constructs (cf. Collins &
Qullian, 1969), we see no good reason to preclude reference
Reference to Generic Instances
The plural variant of
the expression a sedan is a car is sedans are cars. This
variant suggests a representation based on a collection of
generic instances rather than a type.
The generic instance category generalizes over prototypes
and exemplars. It is difficult to distinguish reference to
prototypes from reference to exemplars since they have
much in common. A prototype may be viewed as a washed
out exemplar (some cognitive approaches treat prototype
and exemplar as essentially synonymous). It is a washed out
exemplar in that it is a generalization over the experience of
particular instances of the type. In this respect, a prototype
is more like a type than an instance, making the distinction
between types and instances less clear cut than is typically
assumed. The use of specific lexical items may help to
make the distinction. Consider the sentence the
prototypical car is a sedan. If the expression the
prototypical car actually picks out a prototype for a
referent, and the expression a sedan picks out a type, then
equating a prototype with a type has the effect of defining
the prototype to be of a particular type.
Allen (1986) discusses the semantics of generic NPs
noting that there is no marking for the generic within NP
morphology and that generics have to be inferred from
context. Grammatically a singular object referring
expression is either definite or indefinite. If the referent of
the expression is a prototype or exemplar, then the
reference is generic. In the expression the sedan is a car
where there is no existing referent in the situation model for
the sedan to refer to, the sedan presumably picks out a
generic instance or type.
The motivation for distinguishing prototypes and
exemplars is a cognitive one, although there is
disagreement within the cognitive community as to whether
or not both notions are needed. It may be sufficient to
distinguish generic instances from types in the situation
model without distinguishing prototypes and exemplars.
Reference to Classes, Collections and Masses
Classes, collections and masses complicate reference in
interesting ways. Classes and types are two sides of the
same coin. The type is atomic and has no subparts.
However, the elements of a class are salient and a plural
nominal is used to refer to classes as in all men.
Collections are also referred to by plural nominals as in the
men/all the men where the men/all the men refers to
some salient collection of men, and not to the entire class.
In these expressions, the noun head men denotes the type,
and the specifier and plural grammatical feature determine
the nature of the referring expression (i.e. class or
collection). Masses differ from classes and collections in
that the elements of a mass are not salient. Singular
nominals are used to refer to masses in English.
Mass and plural nouns, but not singular count nouns,
may function as referring expressions without separate
specification. In rice is good for you, rice does not refer
to any specific instance of rice and in books are fun to
read, books does not refer to any specific collection of
books. Both expressions are indefinite. They refer to
something non-specific: a type or generic instance for
rice and a generic collection for books. Reference to a
specific mass or collection requires a definite determiner as
in the rice is ready and the books are fun to read.
The use of a plural nominal to refer to a class or
collection suggests that the members of the class or
collection are cognitively salient and may be separately
represented. This opens up the possibility of either referring
to the class or collection as a whole or referring to the
elements of the class or collection. However, for cognitive
reasons having to do with the limited capacity of humans to
attend to multiple chunks of information (e.g. Miller, 1956),
it is assumed that any linguistic expression may only
introduce a small number of referents into a situation model
(cf. Johnson-Laird, 1983). In the sedans are cars example,
the instantiation of a sedan collection and two generic
instances of a sedan, and a car collection and two generic
instances of a car is the minimal number consistent with the
plurality of the object referring expressions. Given these
referents, it is possible to refer to the collections as a whole,
and it is also possible to pair the members of one collection
with the members of the other collection. These alternatives
correspond to the collective and distributive readings
discussed in Lyons (1977). Lyons presents the example
those books cost $5 which is ambiguous between a
— each book is $5
— and collective
— all the books are $5
— reading. Distributive and collective readings
involve inferential processes operating over collections and
instances which are not part of the grammatically encoded
meaning. However, addition of each to those books cost
$5 each imposes a distributive reading.
We can now see that Johnson-Laird’s representation of
every man owns a car corresponds closely to a
distributive reading (constrained to a small number of
referents). We are also in a better position to consider the
representation of every man. Although expressions with
every are singular, suggesting selection of an arbitrary
instance of a collection, in Everyone is leaving. They are going to
eat., subsequent references are plural. Further, Everyone
is leaving. He is going to eat is infelicitous. There are two
implications of these examples: 1) every instantiates or
references a collection in the situation model, and 2) the
arbitrary referent of every is not salient for subsequent
reference. Even referring expressions with singular a as
in Everyone owns a car. They are indispensable. support
subsequent plural reference, although in this case
Everyone owns a car. It is indispensable. is also
felicitous. This may result from the flipping of the
type/class coin. Subsequent singular reference is to the type
(or generic instance), subsequent plural reference is to the
Reference to Vacuous Instances and Collections
The empty set is a useful notion in set theory. The null
symbol (or empty list) is a useful symbol in the Lisp
programming language. In both set theory and Lisp, these
are actual objects that can be referred to and manipulated.
The grammatical and lexical structure of English strongly
suggests the possibility of referring to a corresponding
empty or vacuous object whose existence is taken for
granted. Yet Martinich (1985, p. 3) argues that the existence
of nothing is an absurd view which rests on a
misunderstanding of how language works. However, not
only does grammar suggest the existence of objects
corresponding to nothing, but it suggests that nothingness
comes in lots of different types and collections. Consider
It is true that a logical representation for expressions like
no man which requires quantifying over every individual
in the model makes little practical sense
but this is taken to be a problem for the logical
representation of the meaning of negative expressions,
rather than as a criticism of negative referring expressions
in language. Allowing negative object referring expressions
to refer to empty or vacuous objects and collections in the
situation model which do not map to any objects or
collections in the mental universe is perhaps the clearest
demonstration of how to simplify the mapping from
referring expression to referent, relative to other
- No one, nobody
- Nowhere, Never
- No man, No dog
- No men, No dogs
Summary and Conclusions
This section presents and supports an expanded ontology of
referential types consistent with Jackendoff’s Conceptual
Semantics, basic principles of cognitive linguistics and
empirical evidence from cognitive psychology. By
expanding the ontology of referential types and introducing
a distinction between situation model and mental universe,
it is possible to simplify the mapping from referring
expression to referent, relative to approaches with a more
limited ontology and single semantic space.
We propose a bi-partite semantic space consisting of a
situation model and mental universe that explains apparent
non-referential uses of referring expressions, along with the
existence of two partial orderings:
Universal (e.g., ∀x ) >
Class (e.g., ∀x (man(x)) or all men) >
Collection (e.g. some/the/all the men) >
Mass (e.g. mankind) >
Instance (e.g. ∃x (man(x)) or a/the man) >
Null (e.g. no man)
Type > Prototype > Exemplar > Token (Individual)
The partial orderings are motivated by the linguistic
expression of referring expressions, cognitive theory and a
computational interest in simplifying the mapping from
referring expressions to corresponding objects and
situations. The partial orderings are not definitive. They
capture important aspects of the mapping from referring
expressions to referents, but there are more dimensions of
meaning involved in this mapping than these two orderings
I would like to acknowledge the support of the Warfighter Readiness Research Division
and its parent organizations: the Human Effectiveness Directorate,
the 711th Human Performance Wing and the Air Force Research Laboratory.
This research would not have been possible without their extensive support.
Additional support has been provided by the Office of Naval Research.
A long-term collaboration with the Cognitive Engineering Research Institute
has also contributed significantly to the research.
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Appendix A: Implementation Details
Every referring expression has a bind-indx to support binding of co-referential expressions
- object referring expression (obj-refer-expr) ~ nominal (noun phrase)
- situation referring expression (sit-refer-expr) ~ clause
- location referring expression (loc-refer-expr) ~ prepositional phrase or nominal with a locative or temporal meaning
- oblique referring expression (obl-refer-expr) ~ prepositional phrase without a locative or temporal meaning
Clause Level Grammatical Function
- focus (i.e. fronted constituent)
- wh-clause focus (wh-focus)
- relative clause focus (rel-focus)
- locative focus (loc-focus)
- subject (subj)
- predicate (or clause) specifier (spec)
- operator (first auxiliary verb in Yes-No-Question and Wh-Question constructions)
- predicate head (head)
- pre-specifier modifier (pre-spec)
- pre-subject modifier (pre-mod)
- pre-head modifier (mod)
- post-head modifier (post-mod)
- conjunction (conj)
- concatenation (concat)
Predicate (Head of Clause) Level Grammatical Function
- predicate verb (pred-verb-type)
- predicate adjective (pred-adj)
- predicate nominal (pred-nom)
- predicate preposition (pred-prep)
- object (obj)
- indirect object (iobj)
- recipient (recip)
- locative complement (loc)
- situation (clausal) complement (comp)
- pre-head modifier (mod)
- post-head modifier (post-mod)
- conjunction (conj)
- concatenation (concat)
Nominal Level Grammatical Function
- object specifier (spec)
- object head (head)
- pre-spec modifier (pre-spec)
- pre-head modifier (mod)
- post-head modifier (post-mod)
- conjunction (conj)
- concatenation (concat)
Clause Level Grammatical Feature
- tense (tense-1)
- finite (fin)
- finite tense (tense)
- past tense (past)
- present tense (pres)
- there is no grammatical feature for future tense in Double-R — future tense is lexically specified
- non-finite (non-fin)
- progressive (prog)
- perfect (perf)
- none (the default aspect is none)
- active (act)
- passive (pass)
- inactive (inact) (the auxiliary be indicates inactive voice)
- modality — the modality feature is just the modal auxiliary
- could, would, should, ...
- positive (indicated by the absence of polarity via none)
- negative (neg)
Nominal Level Grammatical Feature
- number (number)
- singular (sing)
- plural (plur)
- animacy (animate)
- animate (anim)
- inanimate (inanim)
- none (the default gender is none, there is no neuter gender in Double-R)
- person (pers)
- subjective (subj)
- objective (obj)
- genitive (gen) — currently possessive pronouns (e.g. "his"), but not possessive nominals (e.g. "John's") indicate genitive case
- none (the default case is none) — only pronouns indicate case in Double-R
Lexical and Grammatical Hierarchy (Chunk Types)
The Full Ontology
Parts of Speech
Object Referring Expressions
Situation Referring Expressions
Oblique Referring Expressions
Predicate Verb Types
The Goal Hierarchy
Double-R includes a hierarchy of goal types that is used to control processing.
The use of a goal hierarchy is uncommon for ACT-R models. It is more common to use
a state slot to control the execution of productions. However, this practice
is unfortunate. The use of a state slot to control the execution of productions
basically tranforms a production system architecture into a procedural programming language.
The real power of a production system is the fact that any production is eligible to fire
at any time so long as it matches the context. The introduction of a state slot effectively
reduces the number of productions which match the context to just one (or a small number).
Whereas the use of a state slot requires an exact match for a production to be
eligible to fire, the use of a goal hierarchy
allows productions to match the context at different levels of abstraction.
Linguistic Representation Details (Chunks)
This section discusses the chunk representations that are the actual representations used in Double-R,
not the abstracted tree representations that are displayed elsewhere in this document.
These chunk representations rely on the representational capabilities of
the declarative memory system of the ACT-R Cognitive Architecture.
Representing Referring Expressions in Double-R
In the main body of this document,
we discuss the representation of referring expressions with diagrammatic representations
generated from the output of Double-R using a tool called phpSyntaxTree
(Eisenbach & Eisenbach, 2006; Heiberg, Harris & Ball, 2007a).
These representations are simplified in various respects.
In this section we present the full details of the representations using the representational framework of ACT-R.
ACT-R provides a frame-based notation for representing declarative knowledge.
Frames in ACT-R are called chunks (a psychological term used to describe declarative memory elements).
Chunks are organized into a hierarchy of chunk-types. The definition of a chunk-type specifies
the type and its location within the type hierarchy.
The chunk-type inherits slots and default values from its ancestors and can specify additional
slots and default values. When a chunk of a given chunk-type is created,
the chunk is given a unique name and the default values can be overridden.
Referring expressions (refer-expr) inherit from a construction-with-head chunk-type.
The construction-with-head chunk-type includes a head slot with the default value head-indx
which indicates the expectation for a head.
The referring-expression chunk-type adds a specifier slot with the default value none
and a bind-indx slot with the same default value.
When a referring expression chunk is created via lexical projection,
the bind-indx slot will be assigned a value which will either be a unique index
or a previously assigned index (indicating co-reference).
If the referring expression is projected by a lexical item that functions
as a specifier (or operator),
this lexical item will be integrated into the specifier (or operator) slot.
If the referring expression is projected by a lexical item that functions as a head,
this lexical item will be integrated into the head slot.
Depending on the context, additional slots may be assigned values when the referring expression is projected.
For situation referring expressions, this often includes the subject as well as the specifier
(or operator) or head.
Referring expression chunks encode an ordered sequence of grammatical functions
(reflecting normal surface order),
followed by an unordered sequence of grammatical features.
Each grammatical function or feature in the referring expression chunk
is represented as a slot name-slot value pair.
The slot name provides the name of the function/feature and
the slot value is either the name of a chunk that represents the value for the function/feature,
or a literal (string or number).
Using the name of a chunk as the value of a slot introduces a level of indirection
into ACT-R based representations.
The contents of the chunk are not directly accessible from the chunk name.
The chunk is a flat, two-level structure.
This convention aligns with approaches like Minimal Recursion Semantics
(Copestake et al., 2006)
which use indices to support the encoding of complex, nested semantic representations
in a flat logical notation.
It contrasts with linguistic approaches like HPSG
(Sag, Wasow & Bender, 2003) and SBCG (Sag, 2009; Sag, 2010)
in which the value of a slot is not the name of a nested structure,
but the structure itself.
Despite this important distinction
(cf. Ball, 2011b),
the diagrammatic representations omit this level of indirection,
displaying the referring expressions themselves (or other constructions)
as the value of a slot.
For example, in
the altitude restrictions →
the object referring expression (obj-refer-expr) chunk contains a specifier (spec) function slot.
The specifier function is filled by the name of a determiner chunk.
Display of the determiner chunk name, the-determiner-wf-pos, is suppressed in the diagram.
The determiner chunk has a word slot that has the value the-word.
The-word is a special chunk that has no slots.
Chunks without slots function like literals (i.e. they are the leaf nodes in the representations),
except that they can spread activation to other chunks in declarative memory whereas literals cannot
(cf. Ball, 2012a).
To further simplify the diagrams, display of the word slot is also suppressed.
Shown below are the ACT-R chunks created or retrieved during the processing of the expression
the altitude restrictions.
This corresponds to the simplified diagrammatic representation shown above
with the entries in that diagram highlighted (and colored) for cross-reference.
the-ore-0-0 ;; chunk name ore = object referring expression
isa obj-refer-expr ;; chunk type
head obj-head0 ;; chunk name obj-head = object head
spec the-determiner-wf-pos ;; chunk name wf-pos = word-form specific part of speech
bind-indx "5" ;; a string which doesn't spread activation
def "def" ;; a string which doesn't spread activation
number plur ;; name of a chunk with no slots
animate inanimate ;; name of a chunk with no slots
person third ;; name of a chunk with no slots
obj-head0 ;; chunk name obj-head = object head
isa obj-head ;; chunk type
head restrictions-noun-plur-wf-pos-0-0 ;; chunk name
mod altitude-noun-sing-wf-pos-0-0 ;; chunk name mod = modifier
the-determiner-wf-pos ;; chunk name wf-pos = word form specific part of speech
word the-word ;; name of a chunk with no slots
letter-1 tt ;; tt is really t, but t has special meaning in Lisp (i.e. true)
altitude-noun-sing-wf-pos-0-0 ;; chunk name wf-pos = word form specific part of speech
isa noun ;; chunk type
index altitude-indx ;; the index generalizes over the different word forms
word altitude-word ;; name of a chunk with no slots
letter-3 tt ;; tt is really t, but t has special meaning in Lisp (i.e. true)
letter-5 tttwo ;; second occurrence of t
restrictions-noun-plur-wf-pos-0-0 ;; chunk name wf-pos = word form specific part of speech
isa noun ;; chunk type
index restriction-indx ;; the index generalizes over the different word forms
word restrictions-word ;; name of a chunk with no slots
ACT-R chunks for the altitude restrictions
As can be seen, the ACT-R chunks encode more information than is shown in the diagram.
We will not attempt to motivate all the slots in this document
other than to say that they are the result of a grammatical and lexical analysis interacting
with the representational capabilities of ACT-R.
Many of the slots in the lexical chunks (those ending in wf-pos which stands for
word form specific part of speech)
including letter-1 to letter-11, trigram-1 to trigram-11
and p-trigram-1 to p-trigram-6 (i.e. peripheral trigram),
are there to support
ACT-R's spreading activation mechanism for DM retrieval.
Since ACT-R chunks cannot have dynamically added slots, each chunk has the maximum number of these slots.
Although the ACT-R chunks are definitive, they are difficult to examine and diagrammatic representations
have been used in this document for expository purposes.
However, it is important to make a few points:
In addition to referring expressions like the altitude restrictions which refer to objects,
there are referring expressions which refer to situations.
For example, the expression the book is on the table refers to a situation
in which a book is on a table.
This situation referring expression expresses a relationship of being on
that exists between two objects which are themselves expressed as object referring expressions.
In this example, the auxiliary verb is functions as a specifier for a situation referring expression,
just like the functions as a specifer for an object referring expression.
Whereas the indicates definite reference to an object with respect to a situation (and context),
is indicates definite reference to a situation with respect to the temporal context of the situation
as indicated by the present tense of is. The terms clause or sentence are often used to describe
linguistic expressions which refer to situations.
These terms are less problematic than form-based terms like NP, VP, IP, CP,
and they have been used in this document for expository convenience.
Sentences also prove important for the handling of intra-sentential vs. inter-sentential co-reference.
We use the term argument as a generic cover term for referring expressions that participate in
a situation referring expression. Arguments may be categorized in terms of their grammatical function
as subject, object, indirect object and complement
(i.e. an argument other than subject, object or indirect object).
In syntactic treatments, the term complement is often used the way we use the term argument,
except that the syntactic use of the term complement loses its semantic motivation
and correspondence with our use of the term argument, when functional heads, which we reject, are introduced.
- ACT-R chunks encode grammatical (i.e. phrasal/clausal) and lexical constructions in Double-R.
- Chunks may have slots with the string value none which indicates the lack of a value.
This is true of both grammatical function slots (e.g. mod none)
and grammatical feature slots (e.g. gender none).
In ACT-R it is not possible to dynamically add slots to a chunk
(actually the latest version of ACT-R does support dynamic addition of slots).
Because of this,
chunks must contain all the slots that a construction may need.
It is for this reason that slots like modifier (mod), post-head modifier (post-mod), conjunction (conj)
and concatenate (concat) exist even when they lack a value.
The diagrammatic representations suppress the display of such slots.
The fact that a slot has the value none is not grounds for considering the linguistic input
that generated the representation to be ungrammatical.
Double-R does not have a strong notion of grammaticality.
- Grammatical features are associated with lexical items and get projected to the expressions
in which they occur
(Ball, revised 2013; Ball, 2010a).
However, grammatical features are only displayed at the level of referring expressions.
In the diagram, the definite (def) feature comes from the, and the inanimate, plural (plur) and third features
come from restrictions, overriding the features that were incrementally projected from altitude.
The indefinite (indef) feature of restrictions is blocked from projecting by the definite (def) feature of the.
Feature blocking and overriding are mechanisms of context accommodation
(Ball, revised 2013).
- The parent slot is crucial for determining when a construction has been integrated into a higher level construction.
A value of none indicates that the construction is yet to be integrated.
- In the example, the leads to retrieval of an object referring expression directly (i.e. the-ore-0-0)
and functions as the specifier.
There is no need to first retrieve the part of speech chunk for the before projecting
an object referring expression.
The same is true for personal pronouns,
although in this case the pronoun functions as the head instead of the specifier.
Circumventing retrieval of the part of speech chunk for determiners and pronouns and retrieving
an object referring expression directly eliminates a retrieval and speeds up processing.
The need for this speed up is motivated by measurements of adult human reading rates which range
from 200-300 words per minute (wpm) compared to less than 200 wpm for Double-R
(Freiman & Ball, 2010).
The measurement of the processing rate of Double-R is possible because of ACT-R’s support
for measuring cognitive processing time.
Situation referring expressions are typically headed by words which express properties of objects
(e.g. (predicate) adjectives), actions involving objects (e.g. intransitive verbs) or relations
between objects (e.g. transitive verbs, (predicate) prepositions).
Situations headed by predicate nominals differ in that the head is not a relation or property,
although predicate nominals function like properties. Although they function like properties,
predicate nominals are referring expressions. However, they are co-referential with the subject
(i.e. they share the same bind index as the subject).
A possible exception is bare nouns like president in expressions like Obama is president.
In this case, the noun functions as the head of the situation referring expression
(very much like a predicate adjective) and need not project an object referring expression
that is co-referential with the subject.
- The book is sad — predicate adjective
- The man is running — intransitive verb
- The man is kicking the ball — transitive verb
- The book is on the table — predicate preposition
- The man is a chef — predicate nominal
Double-R projects the minimal structure necessary to represent a linguistic expression.
In the case of predicate adjectives and predicate intransitive verbs which do not combine with an object,
the adjective or intransitive verb is directly integrated into the representation as in he is happy:
he is happy
However, Double-R also handles the case where more structure is needed as in he is very happy:
he is very happy
This is achieved in Double-R by projecting a predicate-adjective or predicate-intransitive-verb construction
in parallel with the integration of the adjective or intransitive-verb.
When the more complex construction is needed it replaces the simpler structure
using the context accommodation mechanism.
It is also possible to project the more complex structure only when it is needed,
but this results in slower processing
Language Specific Buffers
ACT-R comes with a collection of buffers (e.g. retrieval buffer, imaginal buffer, goal buffer) that constitute
(at least part of) its working memory (Ball, 2012). These buffers have proved inadequate to support language
analysis, especially with respect to modeling binding and co-reference, and we have added a collection of
language (and grammatical function) specific buffers which include subject, object,
indirect object, wh-focus,
relative-focus and locative-focus. The existence of these buffers is motivated on functional grounds.
They are needed to support binding and co-reference, and language processing more generally.
Although Taatgen & Anderson (2008) argue on theoretical
grounds for limiting functionality and keeping ACT-R tightly constrained, functional considerations are
important in the creation of complex cognitive models and may have theoretical implications as well
(Ball, 2011b, 2012).
Whereas a model which focused on a particular aspect of binding or co-reference might make do with
the existing buffers, a broad coverage model like Double-R that is intended to be functional as well as
cognitively plausible simply does not have the needed architectural resources.
Fortunately, ACT-R 6 supports the addition of buffers (and modules) as a mechanism for extending ACT-R and
we have taken advantage of this capability in our research.
The language specific buffers that have been added to ACT-R give the language analysis capabilities of
Double-R the flavor of a language module.
However, we do not claim that these buffers are fully encapsulated within a language module, and
the language analysis productions in the procedural module which access these buffers are interleaved with
productions which perform other cognitive functions, and which may also access these buffers.
We also do not claim that these buffers are innate.
For example, in languages like Chinese (unlike English), wh-words occur in normal argument position and
a wh-focus buffer may not be needed. We do claim that humans are capable of learning how to buffer information
that may be subsequently needed
— a form of expertise.
Binding and co-reference in language analysis provide concrete examples of this need.
To see how these language specific buffers are needed to support Double-R’s binding mechanism, consider
the processing of
Want is a transitive verb that can optionally take an infinitival complement in addition to or in place of
the object. When the infinitival complement occurs, the subject of the infinitival complement is not expressed
and must be inferred from the matrix clause. There are two possibilities: 1) the subject of the infinitival
complement corresponds to the object of the matrix clause (if there is one), or 2) the subject of
the infinitival complement corresponds to the subject of the matrix clause. To handle these alternatives,
both the subject and object of the matrix clause must be available to support binding by the subject of
to eat. In addition, if the infinitival complement is headed by a transitive verb (e.g. eat), the object
of the transitive verb may also be unexpressed.
In this case, the object may also be inferred from the matrix clause.
- I want it
- I want to eat
- I want you to eat
- I want the cookie to eat
- What do you want me to eat
First, consider the processing of I want it. When I is processed, a nominal corresponding to I is
retrieved from memory (or projected from I), its referent is determined and the nominal is placed in
the subject buffer. (Note that the processing of I does not lead to projection of a clause.
Language is often used to point out objects in the environment and projecting a clause on the basis of
a nominal is not well motivated.)
When want is processed, a declarative clause is projected based on want being a tensed verb and
a subject being available in the subject buffer. The nominal in the subject buffer is integrated as
the subject of the clause. When it is processed, a nominal is retrieved (or projected).
The nominal is integrated as the object of the predicate-transitive-verb construction projected from want
and it is also placed in the object buffer.
The resulting representation is shown below:
I want it →
This example demonstrates the need for a subject buffer to support integration of the subject into
the situation referring expression projected by the main verb under the assumption that the subject does not
project a situation referring expression by itself (for the reasons discussed above).
The reason the object referring expression is placed in a special subject buffer and not in a subject slot
of a chunk in a core ACT-R buffer (e.g. goal, imaginal), is because the grammatical features of
the object referring expression need to be accessible to support binding and co-reference.
If the object referring expression chunk were placed in a slot of a chunk in a buffer, its grammatical feature
slots would not be accessible.
The reason the object referring expression is not placed in a core ACT-R buffer where the grammatical feature
slots would be accessible is because there are an insufficient number of core buffers to hold all
the referring expressions in the matrix clause.
The alternative of storing the object referring expression in declarative memory and retrieving it when
needed has proved functionally unmanageable for handling multiple and chained long-distance dependencies.
For example, in What do you want the boy on the chair by the table next to the girl to eat, binding
the subject of to eat to the boy and the object of to eat to what leads to severe interference
without an object and wh-focus buffer to facilitate this binding.
The processing of I want you to eat proceeds similarly to I want it, up to the processing of to eat.
At this point, the object referring expression retrieved from I is in the subject buffer and
the object referring expression retrieved from you is in the object buffer.
The expression to eat is processed as a multi-word unit which projects an infinitive clause.
The subject of an infinitive clause must be recovered from the linguistic context.
In this example, there are two object referring expressions available:
the subject and the object of the matrix clause. The grammatical default is to prefer to bind the subject of
the infinitive clause to the object of the matrix clause.
This default applies so long as the grammatical features of the object are compatible with the subject of
the infinitive clause. In particular, the subject of to eat is presumed to be animate or human.
The pronoun you projects the animacy feature human, so the default applies and the subject of
the infinitive clause is bound to you.
To support binding, the subject of to eat is represented by an implied object referring expression with
head PRO (the term PRO is borrowed from generative grammar and indicates an implicit subject).
The bind index of PRO is set to match the bind index of you.
Although PRO represents the binding from the subject of the infinitive to the matrix object,
the matrix object itself (not PRO) is placed in an embedded subject buffer, to support further processing.
This example demonstrates the need to retain the object of the matrix clause for binding.
The resulting representation is shown below.
I want you to eat →
In the processing of I want the cookie to eat, the animacy feature of the object is not compatible with
the subject of the infinitive clause. In this case, the alternative of binding to the subject of
the matrix clause is considered.
(Actually, these alternatives are considered in parallel based on ACT-R’s production matching capability
combined with production utility, with the highest utility production which matches the input and
context determining the outcome.) Since the animacy of I is compatible,
the implicit subject of the infinitive clause is bound to the matrix subject.
In addition, the object of the matrix clause is available to be bound by the implicit object of
the predicate-transitive-verb construction projected by to eat and the grammatical features are
compatible with that binding. The implicit object of to eat is represented as an object referring expression
with head trace (the term trace is also borrowed from generative grammar and indicates a displaced object).
This trace element is set to match the bind index of the matrix object and the matrix object is placed in
an embedded object buffer which is distinct from the object buffer.
This example demonstrates the need to retain both the subject and object of the matrix clause
to support binding. The resulting representation is shown below.
I want the cookie to eat →
The grammatical features that get projected from lexical items to referring expressions (Ball, 2010a) are crucial
for determining binding, as are the argument preferences of the verbs want and eat which are
— indicating the expectation for an object.
However, grammatical features are not always definitive. Consider the expression I want it to eat.
The pronoun it can be used to refer to either animate (e.g. dog) or inanimate (e.g. cookie) objects
(and even humans when their sex is unknown as is often the case with babies). In the case of it,
binding and co-reference depend on the actual referent. If the referent of it is a cookie,
then binding the object to it is preferred; if the referent is an animal or human,
then binding the subject to it is preferred. In the absence of an identified referent,
the binding is ambiguous. By default, Double-R treats it as animate and binds the subject.
Double-R doesn’t currently have the capability to use the referent of a referring expression to determine
binding in ambiguous cases.
To motivate the need for retaining the indirect object in a buffer, consider the processing of the expression
I gave him the cookie to eat.
In this example, the processing of him leads to retrieval of an object referring expression which is
integrated as the indirect object of the predicate-ditransitive-verb construction projected by gave.
This object referring expression is also placed in the indirect object buffer. At the processing of to eat,
the default preference is to bind the implied subject of the infinitive clause to the indirect object which
is normally animate or human. It is also preferred to bind the (direct) object to the implied object of
the transitive verb eat. The resulting representation is shown below.
I gave him the cookie to eat →
The processing of intra-sentential infinitival complements provides strong motivation for retaining
object referring expressions which function as arguments in the matrix clause in buffers to support binding
by implied arguments in the subordinate clause. An earlier version of Double-R made use of a fixed size stack
of object referring expressions, but lacked grammatical function specific buffers. This architecture proved
to be functionally inadequate. In the previous example, it is possible for the object referring expressions
for I, him and the cookie to be stacked such that the object the cookie is on top,
the indirect object him is next and the subject I is on the bottom.
While a stack will handle this example, it does not generalize to more complex examples.
Consider I gave him the book on the table in the kitchen to read.
If all object referring expressions (e.g. I, him, the book, the table, the kitchen) are stacked,
then it is not possible to determine the grammatical function of the object referring expressions based on
position in the stack. Further, if the stack is fixed in size (an unbounded stack is cognitively implausible),
it is always possible to generate a linguistic expression which will cause the stack to overflow leading to
the loss of a referring expression that is needed for subsequent binding. Of course, this would be OK if it
matched empirical findings, but it doesn’t appear to.
On the other hand, the stack of object referring expressions is still needed to support the integration of
post-head modifiers. In the example, in the kitchen modifies the table, and on the table modifies
the book. A fixed size stack on the order of 3 or 4 object referring expressions seems a cognitively
reasonable mechanism for handling post-head modifiers which typically modify the preceding
object referring expression, but may also modify earlier expressions
(e.g. in I saw the man on the hill with the binoculars, with the binoculars may modify saw, the man
or the hill, although modifying the hill is semantically dispreferred).
The current model combines grammatical function specific buffers with a stack of the most recent
object referring expressions. Besides being functionally motivated, this architecture is compatible with
empirical evidence of primacy and recency effects.
The grammatical function specific buffers retain the outermost object referring expression in
a deeply modified expression—supporting primacy effects,
while the fixed stack retains the 3 most recent object referring expressions—supporting recency effects.
It is important to note that in this architecture, an object referring expression may constitute the contents
of more than one buffer. In the example above, the cookie fills the object buffer as well as
the most recent object referring expression buffer in the stack. In a sense, the buffers provide pointers
to object referring expressions, except that the contents of the object referring expression are directly
accessible in the buffer without a retrieval from declarative memory.
The processing of wh-questions demonstrates the need for a wh-focus buffer to support binding.
Consider the expression What do you want me to eat?.
The processing of what leads to projection of a wh object referring expression that is put in
the wh-focus buffer. Note that the processing of what does not lead to projection of a wh-question.
(There are wh-constructions like what he said…is true that are not wh-questions.)
The processing of the auxiliary verb do in the context of a wh object referring expression in
the wh-focus buffer leads to projection of a wh-question with a wh-focus function that is filled by
the referring expression in the wh-focus buffer and an operator function that is filled by do.
The processing of you following do results in retrieval of an object referring expression that
is integrated as the subject of the wh-question and this object referring expression is also placed in
the subject buffer.
The processing of want leads to projection of a predicate transitive verb construction that is integrated
as the head of the wh-question. In addition, an implied trace object of want is created and bound to the
wh object referring expression in the wh-focus buffer.
The binding of the trace object to the wh-focus reflects Double-R’s greedy mechanism for modeling
long distance dependencies involving fronted wh words.
Note that if the entire input were What do you want?, the binding of the implied object of want to
the wh-focus is expected.
What do you want →
The processing of me leads to retrieval of an object referring expression.
This referring expression is integrated as the object of want displacing the implied trace that was bound
to the wh-focus. This displacement is an example of the context accommodation mechanism at work.
The processing of to eat leads to projection of an infinitive situation referring expression.
An implicit PRO object referring expression is projected and bound to the matrix object me.
In addition a trace object referring expression is projected and integrated as the object of to eat.
This trace expression is bound to the wh-focus.
What do you want me to eat →
The examples above focus on the importance of representing grammatical features and verb argument preferences
for determining the binding of implicit arguments in complement clauses associated with the main verb want.
There is an additional contrast between the behavior of verbs like want (object control verbs, or better,
object-to-subject control) and verbs like promise (subject control verbs, or better subject-to-subject
control) which affects the binding of implicit arguments.
Control is a central topic in modern linguistic theory (cf. Culicover, 2009).
Consider the following classic examples from Chomsky (1981):
Persuade is an object control verb: the object of persuade determines the binding of the implicit subject of
the infinitival clause to go.
This is the default behavior discussed above for want. Promise is a subject control verb:
the subject of promise determines the binding of the implicit subject. Subject control is the exception
— only a few verbs exhibit this preference. Control is not limited to verbs.
Adjectives functioning as predicates also exhibit control. Consider
- He persuaded me to go
- He promised me to go
(also from Chomsky, 1981).
Subject-to-subject control is the default for (predicate) adjectives.
The subject of eager determines the implicit subject of to please.
Easy is exceptionally a subject-to-object control adjective: the subject of easy determines
the implicit object of to please and the implicit subject of to please is unbound
(e.g. He is easy for someone to please). The examples with adjectives also demonstrate the possibility of
adding an optional clausal complement to clauses containing a predicate adjective, despite the fact that
adjectives do not normally expect a complement.
He is eager and he is easy are both grammatical without the infinitival complement.
- He is eager to please
- He is easy to please
This section motivates the introduction of grammatical function specific buffers (subject, object),
the representation of grammatical features (number, animacy, gender), and the encoding of verb preferences
(transitive vs. intransitive; subject control vs. object control) in order to model the binding of
implicit arguments of complement clauses within Double-R Grammar.
List of Language Specific Buffers and Corresponding Chunk Types
The buffers are highlighted in blue.
- Matrix clause buffers
- Focus Buffers
- Wh-object-referring-expression (e.g. what1 do you want t1?)
- Wh-referring-expression (e.g. where1 did he put the ball t1?)
- That-object-referring-expression (e.g. the book1 that1 you read t1)
- Which-object-referring-expression (e.g. the book1 which1 you read t1)
- Who-object-referring-expression (e.g. the boy who1 PRO1 likes you)
- PRO (e.g. the book1 PRO1 you read t1)
- Loc-referring-expression (e.g. on the table is the book;
here is the book; on Tuesday, the show will air)
- Focus (other)
Whenever a referring expression is in a focus buffer, there is an implicit referring expression (either PRO or t for trace)
that is bound to it.
- Conjunction (e.g. if we can, we should)
- That-comp (that complementizer)
- That-object-referring-expression (e.g. I think that he likes you)
- Argument Buffers
- Object-referring-expression (e.g. John likes Mary)
- Wh-object-referring-expression (who1 PRO1 is going?)
- Situation-referring-expression (e.g. Eating out is expensive)
- Wh-situation-referring-expression (e.g. What he said is true)
- Object-referring-expression (e.g. he gave the boy a cookie)
- Wh-object-referring-expression (e.g. what1 did he give the boy t1?)
- Object-indirect (indirect object)
- Object-referring-expression (e.g. he gave the boy a cookie)
- Wh-object-referring-expression (e.g. who1 did he give t1 a cookie?)
- Situation Referring Expression Buffers
- Predicate (Head of Situation)
- Predicate-verb (e.g. I hit the ball)
- Predicate-preposition (e.g. the book is on the table)
- Predicate-adjective (e.g. he is very happy)
- Predicate-nominal (e.g. he is a man)
- Verb-Intrans (e.g. he laughed)
- Adjective (e.g. he is happy)
- Wh-object-referring-expression (e.g. what1 is he t1)
- Situation-matrix (Matrix clause situation)
The situation-matrix buffer encodes the matrix situation. It is primarily used for intra-sentential processing, but it also determines the dialog act (e.g. discourse statement, discourse question) of the preceding sentence.
- Object Referring Expression Buffer Stack
There are distinct buffers for the object referring expression and the head of the object referring expression.
The reason for this is because it is not possible to access the slots of the head of the object referring expression from
the object referring expression itself. The head slot of the object referring expression only contains the name of the head
chunk. To access the slots of the head, the head must also be available in a buffer. Although grammatical features are
projected from the head to the object referring expression, there is other information in the head that is not projected and
is occasionally needed during grammatical analysis. For example, to incrementally process speeds in those speeds,
it is necessary to determine that the head of the object referring expression which was projected by those is
a demonstrative pronoun (in this case those), so that the demonstrative pronoun can be shifted from the head to
the specifier function (via context accommodation) to allow speeds to function as the head. The information that those
is a demonstrative pronoun is not projected to the object referring expression.
It should be noted that the head is not accessible in any of the grammatical function specific buffers.
The working assumption is that all grammatical information of the head that is needed has been projected to
the object referring expression in the GF specific buffer. If access to the head is necessary, it occurs via the buffer stack.
- Most-recent-child-ore (most recent child object referring expression)
- Wh-object-referring-expression (e.g. what, who,
which and how much/many)
- Most-recent-child-ore-head (most recent child object referring expression head)
- Obj-head = object head (e.g. the altitude restrictions)
- Noun (e.g. the restrictions)
- Rel-head = relational head (e.g. the running of the bulls)
- Most-recent-parent-ore (most recent parent object referring expression)
- Most-recent-parent-ore-head (most recent parent object referring expression head)
- Most-recent-grandparent-ore (most recent grandparent object referring expression)
- Most-recent-grandparent-ore-head (most recent grandparent object referring expression head)
- Situation Referring Expression Buffer Stack
- Most-recent-child-sre (most recent child situation referring expression)
- Situation-Referring-Expression (e.g. the man hit the ball;
what1 did the man hit t1)
- Most-recent-child-sre-head (most recent child situation referring expression head)
- Pred-verb (e.g. I hit the ball)
- Pred-prep (e.g. the book is on the table)
- Pred-adj (e.g. he is very happy)
- Pred-nominal (e.g. he is a man)
- Verb-Intrans (e.g. he laughed)
- Adjective (e.g. he is happy)
- Wh-object-referring-expression (e.g. what1 is he t1)
- Most-recent-parent-sre (most recent parent situation referring expression)
- Most-recent-parent-sre-head (most recent parent situation referring expression head)
- Most-recent-grandparent-sre (most recent grandparent situation referring expression)
- Most-recent-grandparent-sre-head (most recent grandparent situation referring expression head)
- Other Referring Expression Buffers
- Most-recent-lre (most recent location referring expression)
- Most-recent-obl-re (most recent oblique referring expression)
- Embedded Clause Buffers
- Argument Buffers
- Object-referring-expression (e.g. I think he is nice)
- Situation-referring-expression (e.g. I think eating out is expensive)
- Wh-object-referring-expression (e.g. I don’t know who1 PRO1 likes him)
- Wh-situation-referring-expression (e.g. I think what he said is true)
- Object-referring-expression (e.g. I think John likes Mary)
- Wh-object-referring-expression (e.g. I don’t know who1 he likes t1)
- Object-referring-expression (e.g. I think John gave Mary the book)
- Wh-object-referring-expression (e.g. I don’t know who1 he gave t1 it)
- Situation Referring Expression Buffers
- Predicate-type (e.g. I think John likes Mary)
- Sit-refer-expr (e.g. I think John likes Mary)
- Wh-sit-refer-expr (e.g. I know who he likes)
- Discourse buffers
- Dialog Interchange Buffers
- Dialog-intrchng (Dialog interchange)
- Dialog interchange chunk-type
The dialog interchange buffer contains a dialog interchange chunk with slots to represent
a pair of dialog acts and the initiator of the dialog act.
For example, in a request-response dialog interchange, a pointer to the request will fill
the first dialog act slot and the initiator of the request will be specified.
When a response is received, the second dialog act slot will point to the response and the initiator
of the response will be specified.
- Dialog-me (Dialog interchange first person participant)
Dialog participant chunk-type
Dialog participants are the humans engaged in a dialog with the model.
The dialog me buffer retains information from the model’s first person perspective.
It contains info about the current and previous dialog act.
- Dialog-you (Dialog interchange second person participant)
Dialog participant chunk-type
Dialog participants are the humans engaged in a dialog with the model.
The dialog you buffer retains information from the second person perspective.
It contains info about the current and previous dialog act.
- Dialog-not-me-or-you (Dialog interchange third person participant)
Dialog participant chunk-type
Dialog participants are the humans engaged in a dialog with the model.
The dialog not me or you buffer retains information from the third person perspective.
It contains info about the current and previous dialog act.
- Dialog Act Buffers
- Discourse-stmt (Discourse statement)
- Situation referring expression chunk type
df slot = stmt
A discourse statement is a situation referring expression whose discourse function (df) slot
has the value statement (stmt). The actual expression need not be declarative situation referring expression
so long as its use as a statement can be determined.
- Discourse-quest (Discourse question)
- Situation referring expression chunk type
df slot = quest
A discourse question is a situation referring expression whose discourse function (df) slot has
the value question (quest). The actual expression need not be a question situation referring expression
so long as its use as a question can be determined.
- Situation referring expression chunk type
df slot = request
A discourse request is a situation referring expression whose discourse function (df) slot has the value request.
The actual expression need not be an imperative situation referring expression so long as its use as
a request can be determined.
- Situation referring expression chunk type
df slot = response
A discourse response is a situation referring expression whose discourse function (df) slot has the value response.
The actual expression need not be an declarative situation referring expression so long as its use as a request
can be determined.
- Discourse Focus Buffers
Referring expression chunk type
The discourse focus corresponds to the backward looking center in Centering Theory. It is the referring expression
that was the center of the previous sentence. It is typically also the discourse subject. However, the discourse focus
may be distinct from the discourse subject in the following contexts:
1) when there is a focus element that is distinct from the discourse subject (e.g. wh-focus in wh-question).
Referring expression chunk type
The discourse topic is intended to capture the topic of an extended interchange or discourse.
It is more stable than the discourse focus.
- Discourse Level Arguments
- Object referring expression chunk type
The discourse subject corresponds to the underlying subject of the previous sentence
— what is sometimes called the logical subject.
This is usually the subject of the previous sentence, but if the sentence is passivized,
then it also corresponds to either the discourse object or discourse indirect object.
- Object referring expression chunk type
The discourse object corresponds to the object of the previous sentence
- Object referring expression chunk type
The discourse indirect object corresponds to the indirect object of the previous sentence
- Discourse Level Situation Buffers
- Discourse-situation – this buffer doesn’t exist
The discourse situation buffer doesn’t actually exist, but if it did exist, it would correspond to the matrix situation of the previous sentence. However, the matrix situation is sub-typed into dialog acts based on the discourse function (df) grammatical feature. At the end of the processing of a sentence, the matrix situation is put in the corresponding dialog act buffer (e.g. discourse statement, discourse request, discourse response, or discourse question).
- Discourse-pred (Discourse predicate)
The discourse predicate is the predicate of the matrix situation from the previous sentence. This buffer is needed to support co-reference of predicates as in did so in
"John read the book. Mary did so too."