Computational Cognitive Linguistics
Jerome A. Feldman
ICSI & UC Berkeley
1947 Center Street
Berkeley, CA, 94704
jfeldman@icsi.berkeley.edu
Abstract
1 The talk will describe an ongoing project
(modestly named the Neural Theory of
Language) that is attempting to model
language behavior in a way that is both
neurally plausible and computationally
practical. The cornerstone of the effort is a
formalism called Embodied Construction
Grammar (ECG). I will describe the
formalism, a robust semantic parser based
on it, and a variety of applications of
moderate scale. These include a system for
understanding the (probabilistic and
metaphorical) implications of  news stories,
and the first cognitively plausible model of
how children learn grammar.
2 Introduction
Computational Linguistics is sometimes
narrowly identified with statistical corpus studies,
but is much broader. The learning and use of
language are inherently information processing
tasks and any systematic description of them must
be computational. If we want to understand how
human brains acquire and exploit language, we are
driven to computational theories and models that
link neural structure to linguistic behavior.
Cognitive Linguistics provides the basic
mechanisms for explaining human language
processing, but has traditionally been informal and
non-computational. Recent work suggests that a
formally grounded computational cognitve
linguistics can be quite productive.
3 The Neural Theory of Language
For some sixteen years, an interdisciplinary
group at ICSI and UC Berkeley has been building
a theory of language that respects all experimental
findings and is consistent with biological and
computational constraints. The intellectual base for
the project is a synthesis of cognitive linguistics
and structured connectionist modelling. This
involves work from neurobiology through politics
and philosophy (Lakoff 1999). An accessible
overview of the goals and methodology of the
project can be found in (Regier 1996) and further
information is available at:
WWW.icsi.berkeley.edu/NTL
The focus of this talk is computational; how can
an embodied theory of language support scalable
systems for language learning and use. The key to
scalability in any paradigm is compositionality; our
goal in modeling language understanding is to
systematically combine the heterogeneous
structures posited in cognitive linguistics to yield
overall interpretations. We have identified four
conceptual primitives that appear to capture the
suffice for building scalable language
understanding systems: SCHEMA, MAP,
(MENTAL) SPACE, and CONSTRUCTION.
Schemas are language-independent representation
of meanings and constructions are form-meaning
pairs. Constructions are form-meaning mappings
as depicted in Figure 1. Maps and mental spaces
are discussed in (Mok 2004). The ECG formalism
has, in addition to the usual inheritance hierarchy,
an EVOKES relation that makes an outside
structure accessible to a schema through a local
name.
As shown in Figure 1, langauge understanding is
implemented as having distinct analysis and
simulation phases. During analysis, a Semantic
Specification (SemSpec) is created from the
meaning poles of the constructions, and is
essentially a network of schemas with the
appropriate roles filled in.. Unification of
constructions requires compatability of their
embodied semantic scehemas as well as form
matching. Crucially, within this network of
schemas are executing schemas (or x-schemas
Narayanan 1999), which are models of events.
They are active structures for event-based
asynchronous control that can capture both
sequential flow and concurrency.
Simulation is a dynamic process which includes
executing the x-schemas specified in the SemSpec
and propagating belief updates in a belief network
(Jensen 1996, Narayanan 1999). This will be
discussed further in Section 5.
Figure1. Overview of the Comprehension Model
4 Embodied Construction Grammar
The cornerstone of the effort is the formalism
called Embodied Construction Grammar (ECG). In
traditional terms, ECG resembles a unification
grammar like HPSG or LFG and many of the
computational insights carry over. But the central
task of grammar is taken to be accounting for the
full range of language learning and use rather than
the specification of acceptable forms.
Grammars in ECG are deeply cognitive, with
meaning being expressed in terms of cognitive
primitives such as image schemas, force dynamics,
etc. The hypothesis is that a modest number of
universal primitives will suffice to provide the core
meaning component for the grammar. Specific
knowledge about specialized items, categories and
relations will be captured in the external ontology.
As a linguistic formalism, ECG combines the
idea of Construction Grammar as form-meaning
pairings (Croft 2001, Fillmore & Kay 1999,
Goldberg 1995, etc.) with the embodied semantics
of the Cognitive Linguistics tradition (Fauconnier
1997, Lakoff 1999, Langacker 1991, etc.).
Computationally, the central ideas involve
probabilistic relational models (Pfeffer and Koller
2000, etc.) and active knowledge (Bailey 1998,
Narayanan 1999, etc.) along with their reduction to
structured connectionist form and thus to neural
models (Shastri 2002).
A grammar specification in ECG should
simultaneously fill three distinct functions:
capturing linguistic insights, specifying the
analysis phase of a computational system, and
serving as the high level description of the neural
embodiment of the grammar. This has been
achieved in some simple cases and serves as a
major constraint in all ECG efforts.
The deep semantic construction grammar of
ECG also supports a novel style of general robust
parsing. The first phase of analysis is an modified
chunk parser (Abney 1996).
The chunker generates a set of semantic chunks
stored in a chart. The second phase of analysis
extracts the smallest number of  chunks that span
the utterance from the chart, and performs
semantic integration. Their common semantic
structures are merged, and the resulting analyses
are ranked according to the semantic density
metric
Without a complete analysis of an utterance, the
system must infer exactly how a set of local,
partial semantic structures fit together into a
coherent, global analysis of the utterance. The
approach taken is an abductive one in that it
assumes compatible structures are the same and
merges them. This has been shown to work quite
well in a system for modeling the learning of new
constructions (Bryant 2004, Chang 2004).
5 Applications
Narayanan (Narayan 1999) has built a
biologically plausible model of how such
metaphorical uses can be understood by mapping
to their underlying embodied meaning. We assume
that people can execute x-schemas with respect to
structures that are not linked to the body, the here
and the now. In this case, x-schema actions are not
carried out directly, but instead trigger simulations
of what they would do in the imagined situation.
This ability to simulate or imagine situations is a
core component of human intelligence and is
central to our model of language. The system
models the physical world as other x-schemas that
have input/output links to the x-schema
representing the planned action.
 In the computational implementation, the spatial
motion domain (source domain) is encoded as
connected x-schemas. Our model of the source
domain is a dynamic system based on inter-x-
schema activation,  inhibition and interruption.  In
the simulation framework, whenever an executing
x-schema makes a control transition, it potentially
modifies state, leading to asynchronous and
parallel triggering or inhibition of other x-schemas.
The notion of system state as a graph marking is
inherently distributed over the network, so the
working memory of an x-schema-based inference
system is distributed over the entire set of x-
schemas and source domain feature structures. The
KARMA system has been tested on narratives
from the abstract domain  of international
economics. The implemented model has about 100
linked x-schemas, and about 50 metaphor maps
from the domains of health and spatial motion.
These were developed using a database of 30 2-3
phrase fragments from newspaper stories all of
which have been successfully interpreted by the
program. Results of testing the system have shown
that a surprising variety of fairly subtle and
informative inferences arise from the interaction of
the metaphoric projection of embodied terms with
the topic specific target domain structure
(Narayanan, 1999).  Among the inferences made
were those related to goals (their accomplishment,
modification, subsumption, concordance, or
thwarting), resources (consumption, production,
depletion, level), aspect (temporal structure of
events) frame-based inferences, perspectival
inferences, and inferences about communicative
intent.
The ECG formalisms as well as the analyzer
described above play a crucial role in a
computational model of how language
comprehension may drive the acquisition of early
phrasal and clausal constructions (Chang, 2004).
This model takes ECG as the target representation
to be learned from a sequence of utterances in
context. Learning is usage-based in that utterances
are first analyzed using the existing set of
constructions, typically resulting in only a partial
analysis that neither provides complete coverage of
the richer background context nor exploits all the
potential input forms and relations in the utterance.
This incomplete analysis prompts the formation of
new constructions that take up the slack.
Constructions can also be formed on the basis of
similarity (two constructions can merge into a
more general construction) and co-occurrence (two
constructions can compose into a larger
construction).
Besides specifying the means for forming new
ECG constructions, the acquisition model provides
an overarching computational framework for
converging on an optimal set of constructions,
based on a minimum description length principle (
Rissanen 1978) that favors compactness in
describing both the grammar and the statistical
properties of the data. This framework extends
previous work in Bayesian model merging for
lexical acquisition (Bailey, 1997) and the induction
of context-free grammars (Stolcke 1994) to handle
the relational structures and usage-based
considerations madepossible with ECG.
Specifically, the criteria employed favor
constructions that have simple descriptions
(relative to the available meaning representations
and current set of constructions) and are frequently
employed.
The model has been applied to learn simple
English motion constructions from a corpus of
child-directed utterances, paired with situation
representations. The resulting learning trends
reflect cross-linguistic acquisition patterns,
including the incremental growth of the
constructional inventory based on experience, the
prevalence of early grammatical markers for
conceptually basic scenes (Slobin, 1985) and the
learning of lexically specific verb island
constructions before more abstract grammatical
patterns (Tomasello, 1992). For current purposes,
the systems described demonstrate the utility of the
ECG formalism for supporting computational
modeling and offers a precisely specified
instantiation of the simulation-based approach to
language.
Conclusion
  For current purposes, the systems described
above demonstrate the utility of the ECG
formalism for supporting computational modeling
and offer a precisely specified instantiation of the
simulation-based approach to language.
Cognitive Linguistics has developed many
profound insights, but these had not been
formalized and made computationally tractable.
Recent results like these suggest that a
Computational Cognitive Linguistics is both
scientifically productive and a semantic basis for a
wide range of natural language understanding
applications.
6 Acknowledgements
The NTL project has always been a team effort.
Special thanks for this presentation go to John
Bryant, Nancy Chang, Eva Mok, and Srini
Narayanan.

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