Recovering Coherent Intepretations Using
Semantic Integration of Partial Parses
John Bryant
Department of Computer Science
University of California at Berkeley
Berkeley, CA 94720
jbryant@icsi.berkeley.edu
Abstract
This paper describes a chunk-based parser/semantic
analyzer used by a language learning model. The
language learning model requires an analyzer that
robustly responds to extragrammaticality, ungram-
maticality and other problems associated with tran-
scribed language. The analyzer produces globally
coherent analyses by semantically integrating the
partial parses. Each resulting semantically inte-
grated analysis is ranked by its semantic compati-
bility using a novel metric called semantic density.
1 Introduction
Consider the task faced by a child trying to learn
language. The utterances confronting the child are
filled with unknown words and unknown syntactic
patterns. The child’s grammar is in constant flux,
and yet the child extracts (partial) meaning.
Chang (Chang and Maia, 2001) uses acognitively
motivated framework to model the situation chil-
dren find themselves in when they learn language.
The model goes utterance by utterance through a
semantically labeled corpus, and whenever the se-
mantic analysis of an utterance does not supply the
salient relations in the context associated with the
utterance, the model hypothesizes new grammati-
cal mappings to account for the mismatch between
the utterance’s analyzed meaning and its associated
context.
The language analysis that the learning frame-
work relies on exhibits many of the standard is-
sues motivating robust methods. Because the
grammar is changing with every utterance, extra-
grammaticality and ungrammaticality (with respect
to the current grammar) are the norm rather than
the exception. In addition, the child language data
functioning as a training corpus is transcribed from
spoken language. As such, the utterances exhibit
the usual difficulties associated with real language
such as agreement errors, false starts, fragments,
and constituent dislocations.
A task of this nature cannot be handled by tra-
ditional methods of language analysis because they
are too brittle. Robust approaches are much more
appropriate. This paper describes a system imple-
menting such arobust approach. Itcombines chunk-
based semantic analysis with an abductive method
for recovering coherent interpretations from incom-
plete analyses.
Upon encountering an utterance, the system at-
tempts syntactic/semantic analysis using a semantic
chunker. If a complete analysis cannot be found,
the system tries to integrate the remaining semantic
chunks by merging common semantic structures. In
other words, the system leverages the semantics to
robustly combine and interpret recognized, but un-
bound semantic chunks.
Different sets of chunks can be integrated in dif-
ferent ways. To compare how good each pos-
sible integration is, the system uses a heuristic
called semantic density. Integrations that are more
dense are preferred since that suggests high seman-
tic compatibility between the recognized chunks.
The semantic density metric is related to the co-
herence/completeness principles used in LFG (Da-
lyrimple, 2001), but in addition, suggests a graded
notion of semantic compatibility more akin to a no-
tion of gradient grammaticality (Sorace and Keller,
to appear).
The rest of this paper is structured as follows.
The next section describes the basic architecture of
the system. Section 3 provides a brief introduction
to the grammar formalism the system operates on.
Section 4 covers the semantic chunker. Section 5
describes the process and motivation for integrating
chunks using structure merging. Section 6 details
the semantic density metric and a graded notion of
semantic compatibility. The last two sections de-
scribe future directions and conclude the paper.
2 System Architecture
As shown in Figure 1, an important aspect of the
system is the tight integration between the learner
and the analyzer. The analyzer extracts the seman-
tic relations from the utterance that the learner uses
The Language
Learner
Semantic
Chunker
Semantic
Integration
Grammar/
Utterance
Chunk
Chart
Ranked
Analyses
The Language Analyzer
Figure 1: The learner and analyzer form a loop.
The learner hypothesizes constructions to account
for missing relations. The analyzer then uses the
new grammar to analyze subsequent utterances.
to hypothesize new constructions. The learner pro-
vides the analyzer better grammars as time goes by,
thereby making the analyzer more robust. This dy-
namic interaction makes it possible for the system
to learn from its experience.
The language analysis component breaks down
into two phases. First the language learner calls
the semantic chunker with the current grammar and
utterance. The first phase of analysis is semantic
chunking. The chunker generates a set of semantic
chunks stored as a chart.
The second phase of analysis extracts the small-
est number of chunks that span the utterance from
the chart, and performs semantic integration. Their
common semantic structures are merged, and the re-
sulting analyses are ranked according to the seman-
tic density metric (see section 6). This ranked set of
analyses are returned to the learner.
3 The Grammar
The grammar rules used for analysis are represented
using a cognitively motivated grammar formalism
called Embodied Construction Grammar (Bergen
and Chang, 2002). The basic linguistic unit in
any construction grammar(Goldberg, 1995) is the
construction. A construction is a form-meaning
pair. Each pair is a structured mapping from a lex-
ical/syntactic pattern to its corresponding semantic
and pragmatic properties.
Construction grammar rejects the assumption that
syntax and semantic are separate processing mod-
ules(Fillmore et al., 1988). Morphemes, idioms
and standard syntactic constructions like subject-
auxiliary-inversion are all represented by the same
kind of object – the construction. Construction
Grammar defines grammaticality as a combination
of syntactic and semantic well-formedness. Using
Embodied Construction Grammar as the linguistic
substrate therefore requires that syntactic and se-
mantic analysis happen simultaneously.
To describe constructions precisely, Embodied
Construction Grammar (ECG) combines a gram-
mar formalism and knowledge representation lan-
guage within a unification-based framework. This
makes it possible for both constructions and frame-
based, schematic knowledge (Fillmore, 1982) to be
expressed succinctly in the same formalism. Link-
ing the grammar into frame-based meaning is what
makes semantic integration possible (see section 5).
ECGhas twobasic units: the schema and the con-
struction.
 Constructions, as discussed, are form-meaning
pairs, while schemas are used to represent
meaning( like frames or image schemas
1
).
 Schemas and constructions have roles which
can be assigned an atomic value (with  )or
coindexed (with !).
 Schemas and constructions are arranged into
inheritance hierarchies with the subcase of
keyword.
 Theself keyword lets a schema or construction
be self-referential
2
.
To make this more concrete, figure 2 shows
the Throw lexical construction and its associated
schemas. Every construction in ECG has a form
block and a meaning block. These blocks are them-
selves special roles that are accessed using the f and
m subscripts. In the case of the Throw construc-
tion, its form pole constrains its orthography fea-
ture to the string throw. Its meaning pole is type
constrained (using the colon) to be of type (Throw-
Action) schema.
The Throw-Action schema has roles for the
thrower and the throwee. These roles correspond
to the semantic arguments of a throw predicate.
Roles can type constrain their fillers, and in the case
of Throw-Action,thethrower must be of type Ani-
mate while the throwee is constrained to be of type
1
Image schemas are schematic representations of
cognitively-motivated spatial primitives
2
ECG’s self operator is equivalent to LFG’s " operator.
construction Throw
subcase of Verb
form : Word
self
f
.orthography  !”throw”
meaning : Throw-Action
schema Throw-Action
subcase of Action
evokes Cause-Motion-Frame as frame
roles
thrower : Animate
throwee : Physical-Object
constraints
doer  !thrower
doee  !throwee
self  !frame.cause
schema Transitive
evokes Transitive-action as frame
roles
doer : Animate
doee : Entity
constraints
doer  !frame.agent
doee  !frame.patient
schema Cause-Motion-Frame
roles
agent : Animate
theme :
cause : Action
s : SPG
constraints
cause.doer  !agent
cause.doee  !theme
schema SPG
roles
source :
path :
goal :
Figure 2: The throw lexical construction and associ-
ated schemas. This construction defines the mean-
ingofthisverbtobetheThrow-Action schema. The
Throw-Action schema evokes the Cause-Motion-
Frame and their roles are coindexed. The SPG
schema is a structured representation of a path with
a source (starting point), path (way traveled) and
goal (end point).
Physical-Object
3
.
Unique to the ECG formalism is the evokes oper-
3
Clearly these selectional restrictions do not apply in all
cases. One can certainly throw a tantrum, for example. Treat-
ment of metaphorical usage is beyond the scope of both this
paper and the system being described.
ator. The evokes operator makes the evoked schema
locally available under the given alias. The Throw-
Action schema evokes its frame, the Cause-Motion-
Frame schema.
The Cause-Motion-Frame schema is the ECG
representation of FrameNet’s Cause-Motion
frame(Baker et al., 1998; The FrameNet Project,
2004). Because Throw is a lexical unit associated
with this frame, the corresponding Throw-Action
schema evokes the Cause-Motion-Frame schema so
that their roles can be coindexed. In this case, the
thrower is bound to the agent while the throwee is
bound to the theme.
The only commitment an evoking schema makes
when it evokes some other schema is that the two
schemas are related. In this way, The evokes oper-
ator provides a mechanism for underspecifying the
relation between the evoking schema and theevoked
schema. Constraints can then be used to make this
relation precise.
Semantic frames are a good example of where
this ability to underspecify is useful. The lexical
item throw, for example, only profiles some of the
roles in the Cause-Motion frame. Using the evokes
operator and constraints, the Throw-Action schema
can pick out which of these roles are relevant to it.
Evokes thus provides an elegant way to incorporate
frame-based information.
Constructions with constituents are quite similar
to their lexical counterparts with two exceptions.
The first is the addition of a constructional block
to define the constituents. The second is that the
form block is now used to define the ordering of the
construction’s constituents.
Figure 3 shows an example of a construction
with constituents– the active ditransitive construc-
tion. Within the construction grammar literature,
Goldberg (Goldberg, 1995) argues that the ditran-
sitive pattern is inextricably linked with the notion
of giving. This is represented in ECG by constrain-
ing the meaning pole of the ditransitive construction
to be of type Giving-Frame.
The Active-Ditransitive
4
construction has four
constituents, one for each grammatical function. Its
first constituent is named subject, for example, and
isconstrained tobeaRefExp(referring expression)
5
4
This representation is intentionally naive in regards to the
relation between active and passive. Not only is this construc-
tion easier to describe in this form, but the language encoun-
tered by the model is sufficiently simple such that the construc-
tions do not need to be written in full generality. Though for
adult language, thisis not the case. For a detailed description of
how argument structure constructions can be represented com-
positionally see (Bryant, 2004).
5
Referring expressions are constructions with a form pole
construction Active-Ditransitive
subcase of Active-Clause
constructional
subject : RefExp[Animate]
verb : Verb
obj : RefExp
ind-obj : RefExp[Animate]
form
subject
f
meets verb
f
verb
f
meets ind-obj
f
ind-obj
f
meetsobj
f
meaning : Giving-Frame
self
m
 !verb
m
.frame
self
m
.donor  !subject
m
self
m
.recipient  !ind-obj
m
self
m
.theme  !obj
m
schema Giving-Frame
roles
donor : Animate
theme :
means : Action
recipient : Animate
constraints
means.doer  !donor
means.doee  !theme
Figure 3: The Active-Ditransitive construction and
the Giving-Frame representing its meaning pole.
The form block constrains the ordering on the con-
stituents. The meets relation means that its left ar-
gument must be directly before its right argument.
In this construction, for example, the subject con-
stituent must be directly before the verb constituent.
with the semantic type Animate.
4 Semantic Chunking
Chunkers (partial parsers) (Abney, 1996) use finite-
state-machines (FSM) arranged into levels, to reli-
ably recognize nonrecursive chunks of syntax. With
this approach, each finite state recognizer corre-
sponds to a simple syntactic rule. The levels control
the interaction between recognizers, with higher-
level recognizers depending on lower-level recog-
nizers.
The semantic chunker that is integrated into the
language analysis system uses the same processing
scheme as Abney-style partial parsers, extending it
to recognize the syntax and semantics associated
with ECG constructions. This means that syntactic
processing and semantic processing happen simul-
taneously. As a consequence, semantic information
that looks like an NP and with a meaning pole that refers to
something.
is easily integrated to help minimize ambiguity.
Constructions require a very different treatment
than the simple syntactic patterns recognized by
FSMs. In addition to the straightforward extension
of the Abney algorithm to perform unification as
well as using a chart, each construction is compiled
into a construction recognizer.
A construction recognizer searches for its con-
stituents in the input utterance and chart. In ad-
dition to satisfying ordering constraints, candidate
constituents must also satisfy the type and coindex-
ation constraints associated with the construction.
Because of this complexity, construction recogniz-
ers are more complicated than FSMs, implement-
ing a backtracking algorithm to search for compat-
ible constituents. For more information about the
matching algorithm, see (Bryant, 2003).
5 Integration Using Structure Merging
Without a complete analysis of an utterance, the
system must infer exactly how a set of local, partial
semantic structures best fit together into a coherent,
global analysis of the utterance. The approach taken
here is an abductive one in that it assumes compati-
ble structures are the same and merges them.
Motivation for such an approach come from both
linguistics and computational approaches to under-
standing. On the linguistic side, one needs to look
no further than what Paul Kay calls the Parsimony
Principle (Kay, 1987). The Parsimony Principle
states that “Whenever it is possible to identify two
roles in a text, the ideal reader does so”.
On the computational side, information extrac-
tion systems like FASTUS (Hobbs et al., 1996) use
an abductive structure merging approach to build
up templates describing particular kinds of events
like corporate transactions. Their mechanism was
intended to quickly build up consistency across ut-
terances. This approach generalizes FASTUS’ ap-
proach to work on semantic frames within utter-
ances as well as across utterances.
5.1 Structure Merging Examples
This section illustrates how an extragrammatical ut-
terance and an ungrammatical utterance from the
CHILDES corpus (MacWhinney, 1991) can suc-
cessfully be interpreted using semantic integration
through structure merging.
Naomi, a child in the study, wants some flowers.
Herfather then responds withIwillgive you one one
flower with a restart in the description of the final
NP chunk. The result of this utterance’s semantic
chunking analysis is shown in Figure 4.
The analysis generates two semantic chunks
without any links between them. The first chunk
0
2
6
6
6
6
6
4
GiveAction
giver :
1
speaker
givee :
2
"
Entity
distribution : 1
#
recipient :
3
addressee
3
7
7
7
7
7
5
2
6
6
6
6
4
GivingFrame
donor :
1
theme :
2
recipient :
3
means :
0
3
7
7
7
7
5
"
Flower
Distribution : 1
#
Figure 4: A simplified semantic chunk analysis of
I will give you one one flower before semantic inte-
gration. Notice that the Flower schema is not con-
nected to the rest of the analysis.
0
2
6
6
6
6
6
4
GivePredicate
giver :
1
speaker
givee :
2
"
Flower
distribution : 1
#
recipient :
3
addressee
3
7
7
7
7
7
5
2
6
6
6
6
4
GivingFrame
donor :
1
theme :
2
recipient :
3
means :
0
3
7
7
7
7
5
Figure 5: A simplified semantic chunk analysis of
I will give you one one flower after semantic inte-
gration. The Entity and Flower schemas could be
merged because Flower is a subtype of Entity and
they had the same value for the distribution feature.
(the GiveAction and coindexed GivingFrame) corre-
sponds to the I will give you one phrase in which
the Giving frame’s roles are filledbythespeaker,
the addressee and an Entity schema corresponding
to the word one.Theone flower chunk corresponds
to the Flower schema.
Figure 5 shows the integrated analysis. Because
the Entity schema and the Flower schema had com-
patible types
6
and features, the system assumed that
they were the same structure. As a consequence,
semantic integration generates a complete analysis.
A second more complex example illustrates some
of the computational and conceptual issues asso-
ciated with structure merging: Sometime later,
Naomi’s father is reading a book to Naomi when
he utters the following ungrammatical phrase: The
lamb is looking a donkey.
Figure 6 shows the chunk analysis of this utter-
6
Flower is a subtype of Entity
0
2
6
6
6
6
6
6
4
LookAction
looker :
1
"
Lamb
distribution : 1
#
lookee :
2
"
Entity
#
3
7
7
7
7
7
7
5
2
6
6
6
6
6
6
6
4
ScrutinyFrame
cognizer :
1
ground :
2
phenomenon :
"
Entity
#
means :
0
3
7
7
7
7
7
7
7
5
"
Donkey
distribution : 1
#
Figure 6: A simplified semantic chunk analysis of
The lamb is looking the donkey before semantic in-
tegration. Notice that theDonkey schema isnot con-
nected to the rest of the analysis.
ance. This example generates two chunks because
the subcategorization frame of the lexical item look
is not satisfied by the chunk adonkey.
Running semantic integration on this set of
chunks, however, results in two different possible
integrations. As shown in Figures 7 and 8, the
donkey can be merged with the ground or the phe-
nomenon the Scrutiny frame
7
This ambiguity corre-
sponds to whether the missing word in the utterance
was intended to be at or for.
Notice that either integration would be an accept-
able interpretation. In other words, these two in-
tegrations are equivalent in terms of their semantic
acceptability. Certainly, however, leaving the a don-
key structure unmerged isworsebecause moreofthe
core elements of the frame would be unfilled. The
semantic density heuristic (covered in the next sec-
tion) formalizes this intuition.
While conceptually it is satisfying for both inter-
pretations to be acceptable, computationally speak-
ing, it is also worrisome. Taking a single analysis
and turning it into two because of a single ambigu-
ity signals the possibility of structure merging being
NP-hard. For the short utterances associated with
child language, this is not problematic. For adult
7
The FrameNet project defines the Scrutiny frame as: This
frame concerns a Cognizer (a person or other intelligent be-
ing) paying close attention to something, the Ground, in order
to discover and note its salient characteristics. The Cognizer
may be interested in a particular characteristic or entity, the
Phenomenon, that belongs to the Ground or is contained in
the Ground (or to ensure that such a property of entity is not
present) (The FrameNet Project, 2004).
0
2
6
6
6
6
6
6
4
LookAction
looker :
1
"
Lamb
distribution : 1
#
lookee :
2
"
Donkey
distribution : 1
#
3
7
7
7
7
7
7
5
2
6
6
6
6
6
6
6
4
ScrutinyFrame
cognizer :
1
ground :
2
phenomenon :
"
Entity
#
means :
0
3
7
7
7
7
7
7
7
5
Figure 7: A simplified semantic chunk analysis of
The lamb is looking the donkey after semantic in-
tegration. Notice that the Donkey schema is now
merged with the ground role. This corresponds to
the The lamb is looking at the donkey.
0
2
6
6
6
6
6
6
4
LookAction
looker :
1
"
Lamb
distribution : 1
#
lookee :
2
"
Entity
#
3
7
7
7
7
7
7
5
2
6
6
6
6
6
6
6
4
ScrutinyFrame
cognizer :
1
ground :
2
phenomenon :
"
Donkey
distribution : 1
#
means :
0
3
7
7
7
7
7
7
7
5
Figure 8: A simplified semantic chunk analysis of
The lamb is looking the donkey after semantic in-
tegration. Notice that the Donkey schema is now
merged with the phenomenon role. This corre-
sponds to the The lamb is looking for the donkey.
language, however, this could be a serious issue. As
such, an open area of research is how to design ap-
proximate merging strategies.
6 Semantic Density
The key insight behind our approach to making the
integrated analyses is the realization that every ut-
terance is trying to communicate a scene (more for-
mally speaking, this scene is a semantic frame).
Nowassuming that a better analysis is one that more
fully describes the scene, one way to compare anal-
yses is by how completely specified the frame is.
Those analyses that fill out more of the frame roles
should be preferred to those that fill out fewer roles.
2
6
6
6
6
4
Commercial-Event 1
buyer : Harry
seller : Bill
goods : acar
price :
3
7
7
7
7
5
2
6
6
6
6
4
Commercial-Event 2
buyer : Harry
seller : Bill
goods : acar
price : 1500
3
7
7
7
7
5
Figure 9: The semantic density metric used to
compare two semantic analyses each containing a
Commercial-Event frame. The Commercial-Event
frame on the top has a semantic density score of
.75 and the Commercial-Event on the bottom has a
score of 1. Thus the second frame would be consid-
ered better because more of the frame elements are
filled.
This is the motivation for the ranking heuristic that
we call semantic density.
Semantic density compares constructional anal-
yses based upon their semantic content. Analyses
that have a higher ratio of filled slots to total slots in
their semantic analysis are considered better analy-
ses according to semantic density. Figure 9 shows
a simple example of the semantic density metric in
use.
Let’s reconsider the example from the last sec-
tion regarding The lamb is looking a donkey.In
that case, there were two possible integrations, one
where the donkey was the ground and the other
where the donkey was the phenomenon. Applying
the semantic density metric to those two competing
analyses shows that they have equivalent semantic
density. This is consistent with the intuition that ei-
ther analysis is an equally acceptable interpretation
of the input utterance.
The higher-level point here is that there are many
ways to semantically analyze a given utterance, and
some ways are better than others. While the two
semantically integrated interpretations of the lamb
sentence were equally good, both were better than
the unintegrated utterance. Given a preference for
one interpretation over the other, it makes sense to
consider semantic interpretation to be a graded phe-
nomenon much like grammaticality.
Keller (Sorace and Keller, to appear) defines the
graded nature of grammaticality in terms of con-
straint reranking within an optimality theory con-
text. Since structure merging and semantic den-
sity also define a gradient, they could also be stated
within an optimality framework, with the most
dense analyses being considered optimal.
7 Related Work
Semantic analysis has a long tradition within NLP.
While a broad overview describing logic, frames
and semantic networks is beyond the scope of this
paper
8
, this work builds on the frame-based tradi-
tion as well as the tradition of robust analysis to
make progress towards robust semantic analysis.
One related traditional approach is Lexical Func-
tional Grammar ((Dalyrimple, 2001)). LFG intro-
duced notions of completeness and coherence for
its feature structures. These principles (intuitively
speaking) require that certain features of an analy-
sis (including all the semantic arguments of a pred-
icate) be filled before an analysis can be considered
well formed. Such a constraint is akin to a binary
version of the semantic density metric.
Structure merging also builds on historical work.
Even before FASTUS, the employment of the parsi-
mony principle within understanding systems goes
back to the completely unrelated FAUSTUS sys-
tem (Norvig, 1987). Norvig’s work, however, used
graph based algorithms within a semantic network
to perform abductive inference.
8 Future Work
While implementation is complete, only initial test-
ing of the analyzer against the CHILDES data has
been started. To get more complete results, a test
grammar covering the parents’ utterances must be
completed (or learned). Once this has been fin-
ished, the number of semantic relations correctly
extracted with and without structure merging can be
measured.
If verified, the ideas in this paper would have to
be appropriately extended to adult language. The
structure merging phase, for example, would have
to redefined to use an approximate search algo-
rithm find a good integration. Investigation of the
approach described by Beale (Beale et al., 1996)
seems promising.
Extremely intriguing is the extension of the se-
mantic density metric. Currently, it is merely a
first approximation of semantic preference. One
obvious direction is weight different frame roles
in accordance with lexical preferences. Accord-
ing to FrameNet data for the lexical item look,
the phenomenon frame role is 50% more likely to
be expressed than the ground role (The FrameNet
Project, 2004). By including such preferences, a
more complete notion of semantic preference can
8
See (Jurafsky and Martin, 2000) for such an overview or
(Allen, 1995) for an introduction with logic.
be defined. Narayanan and Jurafsky (Narayanan
and Jurafsky, 1998) take the first steps in this direc-
tion, integrating syntactic and semantic preferences
within a probabilistic framework for a small scale
preference task.
9 Conclusion
The analysis task found within a learning model
exhibits all the canonical problems that motivate
robust parsing algorithms: extra/ungrammaticality,
restarts, agreement issues etc. Because of these
reasons, the language learning setting seems like a
good testing ground for robust analysis algorithms.
One such algorithm has been described in this pa-
per. Thealgorithm is unique because of its heavy re-
liance on semantics. It uses knowledge at all phases
of its analysis, from when it recognizes construc-
tions using a semantic chunking approach, to when
it merges the chunks’ common semantic structures.
This paper also takes a small step toward defin-
ing a gradient notion of semantic analysis. Not all
analyses of an utterance are created equal. One sim-
ple approach to comparing semantic interpretations
is by measuring how completely they specify their
schemas and frames. This is how the semantic den-
sity metric works.
More important than the particulars of these al-
gorithms, however, is the idea that a learning model
and an analysis model should be tightly coupled.
Such a model makes it possible for a language un-
derstanding system to learn new language from ex-
perience. If such an idea can come to fruition, this
would be the most robust language analysis algo-
rithm of all.

References
Steven Abney. 1996. Partial parsing via finite-state
cascades. In Proceedings of the ESSLLI ’96 Ro-
bust Parsing Workshop.
James Allen. 1995. Natural Language Understand-
ing. The Benjamin/Cummings Publishing Com-
pany.
Collin F. Baker, Charles J. Fillmore, and John B.
Lowe. 1998. The Berkeley FrameNet Project. In
Proc. COLING-ACL, Montreal, Canada.
Stephen Beale, Sergei Nirenburg, and Kavi Ma-
hesh. 1996. Hunter-gatherer: Three search tech-
niques integrated for natural language semantics.
In Proceedings of AAAI-96, Portland, Oregon.
Benjamin Bergen and Nancy Chang. 2002. Em-
bodied construction grammar in simulation-
based language understanding. Technical Report
TR-02-004, ICSI. To appear in Oestman and
Reid, eds., Construction Grammar(s): Cogni-
tive and Cross Lingusitic Dimensions. John Ben-
jamins.
John Bryant. 2003. Constructional analysis. Mas-
ter’s thesis, UC Berkeley.
John Bryant. 2004. Towards cognitive, com-
positional construction grammar. Available at
www.icsi.berkeley.edu/jbryant.
Nancy Chang and Tiago Maia. 2001. Learning
grammatical constructions. In Proceedings of the
Conference of the Cognitive Science Society.
Mary Dalyrimple. 2001. Lexical Functional Gram-
mar. New York: Academic Press.
Charles Fillmore, Paul Kay, and M. C. O’Connor.
1988. Regularity and idiomaticity in grammat-
ical constructions: the case of let alone. Lan-
guage, 64(3):501–538.
Charles Fillmore. 1982. Frame semantics. In Lin-
guistics in the Morning Calm, pages 111–138.
Linguistics Society of Korea.
Adele Goldberg. 1995. Constructions: AConstruc-
tion Grammar Approach to Argument Structure.
University of Chicago Press.
Jerry Hobbs, Douglas Appelt, John Bear, David
Israel, Megumi Kameyama, Mark Stickel, and
Mabry Tyson. 1996. Fastus: A cascaded finite-
state transducer for extracting information from
natural-language text. In Roches and Schabes,
editors, Finite State Devices for Natural Lan-
guage Processing. MIT Press.
Daniel Jurafsky and James Martin. 2000. Speech
and Language Processing. Prentice Hall.
Paul Kay. 1987. Three properties of the ideal
reader. In Roy Freedle and Richard Duran, ed-
itors, Cognitive and Linguistic Analyses of Test
Performance, pages 208–224. Ablex Publishing
Corporation.
Brian MacWhinney. 1991. The CHILDES project:
Tools for analyzing talk. Erlbaum, Hillsdale, NJ.
Srini Narayanan and Daniel Jurafsky. 1998.
Bayesian models of sentence processing. In Pro-
ceedings of the Conference of the Cognitive Sci-
ence Society.
Peter Norvig. 1987. FAUSTUS. Ph.D. thesis, Uni-
versity of California at Berkeley.
Antonella Sorace and Frank Keller. to appear. Gra-
dience in linguistic data. Lingua.
The FrameNet Project. 2004.
http://www.icsi.berkeley.edu/framenet.
