Research Proposal
Integrating Cross-Modal Context
for Syntactic Disambiguation
by
Patrick McCrae
Supervisor: Prof. Wolfgang Menzel
CINACS Graduate College
Department of Informatics
Hamburg University
Vogt-Kölln-Straße 30
22527 Hamburg, Germany
E-Mail: [email protected]
Phone: +49 . 40 . 428 83 - 23 60
Research Proposal
Patrick McCrae
___________________________________________________________________________
0. Table of Contents
0.
1.
2.
3.
Table of Contents ................................................................................................................. 1
Abstract ................................................................................................................................ 2
Introduction .......................................................................................................................... 3
Literature Review................................................................................................................. 6
3.1.
Current Approaches to PP Attachment Disambiguation............................................ 6
3.2.
Thematic Role and Verb Meaning ............................................................................. 7
3.3.
Context and World Knowledge in Thematic Role Assignment................................. 8
3.4.
Existing Semantics-Integration Efforts ...................................................................... 9
4. Research Focus................................................................................................................... 10
5. Method ............................................................................................................................... 13
5.1.
Instruments ............................................................................................................... 13
5.2.
Material .................................................................................................................... 13
5.3.
Setup......................................................................................................................... 13
5.4.
Experimental Procedure ........................................................................................... 14
6. Work Plan........................................................................................................................... 16
I. Analysis Phase.............................................................................................................. 16
II. Implementation Phase .................................................................................................. 17
III.
Investigation Phase................................................................................................... 17
IV.
Documentation Phase............................................................................................... 18
7. References .......................................................................................................................... 19
Integrating Cross-Modal Context for Syntactic Disambiguation
1
Research Proposal
Patrick McCrae
___________________________________________________________________________
“(…) the notion of plausibility is incomplete in
the absence of a specification of a particular
context (…)“
Crain & Steedman (1985, p. 335)
1. Abstract
Most natural language utterances contain ambiguity, which results in semantic
underspecification. Despite the presence of ambiguity, human communication still succeeds
in most cases and even displays a remarkable robustness – quite in contrast to the majority of
natural language applications. The reason is that in processing an ambiguous utterance
humans also integrate information from sources other than the utterance itself, linguistic or
non-linguistic in nature, and thus gain additional knowledge which results in an improved
disambiguation accuracy. This additional extra-sentential knowledge can be obtained from
sensory input, e. g. via cross-modal perception, or can already be present in the form of
context knowledge or world knowledge. While a wide range of studies has systematically
investigated the impact of context and world knowledge upon structural disambiguation in
human sentence processing, there are surprisingly few attempts to model the integration of
context and world knowledge in natural language processing applications.
Certain types of structural ambiguity cannot be resolved on linguistic grounds alone. One
such example is prepositional phrase (PP) attachment in German which we intend to study.
For German, PP attachment accuracies as high as 92% have been reported using statistical
approaches. Since statistical approaches generalise over occurrence frequencies observed in
large text corpora, they must fail in cases in which structural decisions depend exclusively on
the individual semantic constellation defined by utterance context.
We therefore hypothesise that integrating extra-sentential context and world knowledge into
syntactic constraint dependency parsing will improve the accuracy of structural ambiguity
resolution in context-sensitive cases such as German PP attachment. To prove our point we
will model context and world knowledge and study the effect of their integration into an
existing constraint-based parser for German.
Integrating Cross-Modal Context for Syntactic Disambiguation
2
Research Proposal
Patrick McCrae
___________________________________________________________________________
2. Introduction
This proposal outlines a research project on structural ambiguity resolution by means of
context and world-knowledge integration. The introductory section will provide the
motivation and frame for the proposed research. The ‘Literature Review’ section will present
current research literature of relevance to the topic. Section 2 will describe the focus of the
proposed project in detail and our methodology will be outlined in section 5. Finally, we
provide a high-level work plan in section 6.
We will now give an overview over the motivation for the proposed research project and
introduce the key concepts addressed in the course of the project.
Considering the complexity of factors contributing to successful natural language interactions,
human language processing is surprisingly robust against noisy signals such as ambiguous or
ungrammatical input – in fact, much more robust than the majority of present-day natural
language processing (NLP) systems. One of the reasons for this robustness is attributed to the
access in human sentence processing to additional information from sources other than the
linguistic material at hand alone.
In the computation of an utterance’s overall meaning humans do not only analyse the
linguistic material in isolation but also incorporate additional linguistic and extra-linguistic
information to establish reference and resolve ambiguity further. Typical sources of additional
knowledge accessed include cross-modal information from other perceptual senses (Knoeferle
2005), discourse history, context (Crain & Steedman 1985) and common sense or world
knowledge (Lieberman et al. 2005). All of these additional sources of knowledge assist in
determining sentence context and thus the resultant sentence meaning which is computed
from the overall context evaluated.
An extensively studied example for syntactic ambiguity is prepositional phrase (PP)
attachment which is of particular interest because in many cases the resolution of structural
ambiguity cannot be performed on purely linguistic grounds alone. Consider (S1).
(S1)
The man saw the woman with a telescope.
(S1) has at least two readings, depending on which constituent the prepositional phrase with a
telescope is considered to refer to, or – in structural terms – which constituent it is considered
to attach to. In the first reading, with a telescope modifies the act of seeing and functions as
the instrument for seeing. In the second reading, the term telescope modifies the woman and
thus functions as comitative expressing the concept of companionship. In semantics these
different functions are labelled by thematic roles, in this case INSTRUMENT or COMITATIVE,
respectively.
Observe that from a purely linguistic point of view both readings and structural interpretations
are equally valid. A conclusive attachment decision requires additional, extra-linguistic
information which may be provided from other sources such as context. A key source of
context information in human communication is cross-modal sensory input (Watanabe 2001).
Given such context information – e. g. by additionally seeing the image of a man looking
through the telescope – we can favour one reading over the other and arrive at the dependency
Integrating Cross-Modal Context for Syntactic Disambiguation
3
Research Proposal
Patrick McCrae
___________________________________________________________________________
structure representations (D1.1) for the thematic role interpretation as INSTRUMENT and
(D1.2) for the interpretation as COMITATIVE.1
Context: Man holding the telescope (INSTRUMENT)
OBJA
SUBJ
(D1.1)
The
man
woman with
saw the
DET
PN
a telescope.
DET
DET
PP
Context: Woman holding the telescope (COMITATIVE)
SUBJ
(D1.2)
The
DET
man
PP
OBJA
saw the
woman
DET
PN
with a telescope.
DET
Another important source of extra-linguistic knowledge accessed for structural ambiguity
resolution in addition to context information is world knowledge. Consider (S2).
(S2)
The man saw the woman with a hat.
Despite (S2)’s structural similarity with (S1), (S1)-like structural ambiguity does not arise.
The PP’s semantic reference – and hence its structural attachment– are directed by our world
knowledge about the use of hats which typically do not function as instruments for seeing.2 In
(S2) world knowledge rather than contextual knowledge has helped us constrain the range of
structural interpretations and possible readings.
Let us now look in more detail at PP attachment as one typical source of structural ambiguity.
While a number of techniques for handling PP attachment exist in NLP, the majority of these
approaches rely on lexical or syntactic properties of the input sentence or statistical
1
As an aside note that some languages like Croatian lexically and morphologically mark the difference between
the thematic roles of INSTRUMENT and COMITATIVE, thus effectively removing the structural ambiguity found
in the English sentence (S1). In machine translation from English to Croatian the explicit marking required in the
target language would obviously necessitate prior disambiguity of the input sentence to be able to decide
between translations (T1.1) or (T1.2).
(T1.1) teleskopom
‘with (INSTRUMENT) the telescope’
(T1.2) sa teleskopom
‘with (COMITATIVE) the telescope’
2
It would, of course, be possible to construct an – admittedly very contrived – context in which this sentence
refers to a special kind of hat that functions as an instrument of seeing and hence would indeed permit the
instrumental reading in this case. We shall, however, disregard such extremely far-fetched contexts in the
subsequent course of our argument.
Integrating Cross-Modal Context for Syntactic Disambiguation
4
Research Proposal
Patrick McCrae
___________________________________________________________________________
distribution patterns of the preposition relative to its attachment constituent (for details cf.
section 3.1). Since all of these approaches are based on properties of the sentence material,
none of them are capable of incorporating supra-sentential information such as sentence
context or world knowledge. Also, the syntactic and lexical properties have been evaluated
they remain static and hence insensitive to dynamic changes in context.
If we wish to enable a natural language system to make informed decisions on PP attachment
dynamically, we need to provide the basis for such decisions by making suitable
representations of context and world knowledge available for integration into the syntactic
decision process.
We therefore hypothesise that integrating extra-sentential context and world knowledge will
improve the accuracy of structural ambiguity resolution for PP attachment decisions in
constraint dependency grammars.
The aim of the proposed research is to model and implement the integration of contextual and
world knowledge into cdg, an existing constraint-based syntax parser for German and to
investigate the impact upon parsing accuracy thereof.
The successful integration of extra-linguistic knowledge into syntactic parsing would be of
interest for its cognitive proximity to current models of human sentence processing. In
particular, it would be interesting to investigate whether robustness effects due to cross-modal
compensation in human sentence processing, such as robustness to ungrammatical or
incomplete input, can be replicated in the parser.
Successful context integration could be employed to support a broad range of natural
language processing applications, some of which include:
ƒ Improved semantic resolution of input sentences to machine translation from
languages without explicit INSTRUMENT role marking to languages with such marking.
ƒ Improved natural language understanding by removing structural ambiguity with
information from extra-linguistic sources.
ƒ Finer differentiation for information retrieval in semantic search applications. A
possible query which otherwise would be swamped by the number of irrelevant search
results in a semantically insensitive search would be (Q1), i. e.: a query term to search
for all web pages on which the concept of telescope is mentioned but in which the
telescope does not function as an INSTRUMENT.
(Q1)
telescope AND (NOT INSTRUMENT)
Integrating Cross-Modal Context for Syntactic Disambiguation
5
Research Proposal
Patrick McCrae
___________________________________________________________________________
3. Literature Review
This section provides an overview over the current state of research literature pertaining to the
integration of semantics in structural disambiguation. We present an overview over current
approaches to the structural disambiguation for PP attachment and, as an alternative approach,
present arguments for thematic role assignment based on verb meaning. We then outline the
role of context and world knowledge in thematic role assignment and report on the attempt to
differentiate between context and world knowledge. Finally, we review in brief other attempts
to integrate context and world-knowledge into syntactic parsing.
3.1.
Current Approaches to PP Attachment Disambiguation
The difficulty of correct PP attachment in (S1) could easily be removed if we had a heuristic
at hand allowing us context-sensitively to assign the correct semantic role to the PP. In fact,
correctly assigning the thematic roles is an equivalent approach to structural disambiguation.
Clearly, such a heuristic needs to consider all aspects of semantics known for a given
utterance. Most PP attachment heuristics employed in NLP today, however, typically are
based on the syntactic or syntacto-lexical properties of the sentence material rather than the
contextual semantics needed for thematic role assignment. Some of the central techniques for
PP attachment disambiguation include:
1. Syntactic Approaches
a. Right Association/Late Closure
b. Minimal Attachment
2. Syntacto-Lexical Approaches
a. Statistical Methods based on Machine Learning
b. Case Frames
Syntactic approaches are based on structural properties of the overall sentence.
Right association/Late Closure interprets a new constituent as being part of the current
constituent under construction rather than as part of some constituent higher in the parse tree
(Allen 1995).
Minimal Attachment disambiguates PP attachment to afford a structure with the least number
of nodes in the parse tree (Allen 1995).
These purely structural approaches appeal for their parsimony and may constitute a
reasonable heuristic to start from. However, they clearly fail in cases of context-driven
attachment decisions between structurally equally valid alternatives. In (S1), both attachment
positions are structurally acceptable; yet, only one attachment disambiguation is licensed by
context. There is also empirical evidence against Minimal Attachment in human sentence
processing from reading times on structurally ambiguous sentences (Taraban & McClelland
1988).
Integrating Cross-Modal Context for Syntactic Disambiguation
6
Research Proposal
Patrick McCrae
___________________________________________________________________________
Syntacto-Lexical approaches are based on the sentence-focused lexical properties of
individual words in the sentence under investigation. While these approaches may include
some degree of semantics, they only focus on sentence-internal semantics provided by the
linguistic material.
Statistical methods employ machine learning to make predictions for PP attachment
disambiguation based on patterns extracted from large text corpora. These approaches will
favour attachment in agreement with what has been found to exist most frequently in the
corpus. With reported attachment accuracies of 92% for German (Foth & Menzel 2006a) and
94.9% (Lüdtke & Sato 2003 as cited in Calvo & Gelbukh, 2004, p. 207) to 95.77% (Kudo &
Matsumoto 2000 as cited in Calvo & Gelbukh, 2004, p. 207) for English, statistical
approaches to PP attachment are the most successful and most frequently used ones in largecoverage parsers today. Note, however, that the statistical approaches, too, are blind to
context and will provide a static, corpus-based decision rather than a dynamic one based on
individual utterance context.
Finally, case frames constitute an approach to base PP attachment decisions on the
formalisation of the verb’s syntactic and semantic attachment requirements anchored in the
lexicon. With case frames, the attachment decision is based on the verb’s syntactic argument
selection criteria. These may be extended to include semantic constraints to permit an
evaluation of semantic fit for the constituents in question. This model is supported by
experimental evidence from priming which indicates that syntactic schemas are activated
during sentence processing (Auble & Franks 1983). To our knowledge, however, present-day
case-frame implementations do not include supra-sentential context or world knowledge into
their assessment of semantic fit. We therefore propose to extend the case frame model to
include context and world knowledge as key components into the assessment of semantic fit
in thematic role assignment.
3.2.
Thematic Role and Verb Meaning
As a first semantically enhanced case-frame approach to thematic role assignment it might
appeal to analyse the verb’s and the PP’s noun phrase (NP) properties to assess whether the
PP can take some thematic role, say INSTRUMENT, for the given verb. (S4.1) and (S4.2)
clearly illustrate the challenge of this task as the same NP can be a good INSTRUMENT for one
sense of the polysemous verb (S4.1) but not for another (S4.2).
(S4.1)
He cut the apple off [with a knife]NP: INSTRUMENT.
Context: Picking an apple.
(S4.2)
? He cut her off [with a knife]NP: INSTRUMENT
Context: A conversation.
Attempts have been made in the literature to define feature-based thematic role hierarchies as
a basis for the decision on an NP’s suitability in a given thematic role slot (Grimshaw 1990,
and Simpson 1991, both as cited by Mylne, 1999, p. 1). The most frequently investigated NP
feature is animacy (e. g. Trueswell et al. 1994) whose importance in human sentence
processing has also been demonstrated using ERPs (Kuperberg 2006).
To our knowledge, however, none of the feature-based categorisation approaches has
achieved coverage over a broader range of verbs, let alone generality. We interpret this lack of
generalisability of thematic role properties an expression of the strong semantic dependence
of possible thematic role fillers on verb argument structure. In assigning the totality of all
Integrating Cross-Modal Context for Syntactic Disambiguation
7
Research Proposal
Patrick McCrae
___________________________________________________________________________
thematic selection criteria to the verb, Dowty (1989) has effectively disassembled all hopes of
constructing a generalised, i. e.: verb-independent, feature-based thematic role hierarchy.
Dowty’s approach is supported by evidence from eye-tracking experiments suggesting that
the thematic roles AGENT, PATIENT and INSTRUMENT are intrinsically integrated into a verb’s
generalised situation schema (Ferretti et al. 2001) – while other roles such as LOCATION are
not. Ferretti et al. conclude that thematic role knowledge is tightly coupled to verb meaning
via the verb’s schema information. We take this experimental evidence as motivation to
model thematic role assignment in close dependency of verb meaning and verb schemas and
acknowledge the need to step beyond sentence-internal semantic relations to consider
additional knowledge about utterance context and the world. Without considering these kinds
of extra-sentential information, cases such as (S1) will remain structurally undecidable to a
parser.
3.3.
Context and World Knowledge in Thematic Role Assignment
It is not a new claim that context and world knowledge are required for thematic role
assignment. McCawley has argued as early as 1968 (McCawley 1968) that selectional
restrictions must make reference to world knowledge and that some of this knowledge will be
verb-specific. McCawley’s selectional restrictions effectively are the verb’s perspective on
thematic role assignment. Crain and Steedman (1985) also propose to turn away from
structural to a semantic and context-based approach for the resolution of local ambiguity in
natural language. Their position arises from experimental evidence demonstrating that
thematic role assignment can be context-directed for English reduced relatives (Crain &
Steedman 1985). Their findings are further supported by other techniques such as eyetracking experiments (Trueswell & Tanenhaus 1994).
While the importance of context in thematic role assignment is unchallenged, the question
remains how strong its contribution to ambiguity resolution exactly is. Trueswell and
Tanenhaus (1994) argue to take into account both the strength and the local relevance of
contextual constraints. The weighted constraint model in cdg will permit us to do exactly that:
by appropriately weighting the constraints which integrate contextual information we can
determine their impact upon the overall parsing decision. cdg thus permits us to investigate
varying degrees of context sensitivity for structural disambiguation decisions.
It remains to be defined how to distinguish between the two kinds of extra-sentential
knowledge mentioned, namely context knowledge and world knowledge. World knowledge
can be understood to comprise all our notions of what kinds of objects and events occur in the
world around us. Schema knowledge accessed in thematic role assignment is considered a
special form of world knowledge on stereotypical situations built on past experience (Chwila
& Kolk 2005). While context intuitively appears to be more situational and world knowledge
more persistent, there really is a continuum between context and world knowledge. Data from
ERP studies comparing integration patterns for context and world knowledge support the
view that a clear cut-off point between semantic and world knowledge cannot be identified
(Chwilla & Kolk 2005).
Crain and Steedman (1985) also point out that each conversational participant only has one
model of the universe of discourse. We interpret this as an implicit suggestion that there is
also just one framing knowledge representation against which utterances are interpreted. Our
approach to model and access context and world knowledge in a single, unified representation
is based upon this line of argument.
Integrating Cross-Modal Context for Syntactic Disambiguation
8
Research Proposal
Patrick McCrae
___________________________________________________________________________
3.4.
Existing Semantics-Integration Efforts
Given the influence of context and world knowledge upon thematic role assignment it is
surprising to see the comparatively small number of successful context-integration
implementations reported in the NLP literature. One of the reasons for this will be the relative
successfulness of approaches which are less complex to implement such as statistical
approaches. Another reason, of course, will be the daunting complexity to implement such
systems.
The following projects are presented in brief because they were – at least in part – successful
in making contextual and world or common sense knowledge available for NLP. These efforts
may prove helpful in our endeavours of basing structural disambiguation decisions on suitable
representations of context or world knowledge. The following comparison is based on Liu &
Singh (2004) and includes updated figures from the websites quoted.
1. WordNet
A database of words, primarily nouns, verbs and adjectives, organised into discrete
senses, that are hierarchically linked by a set of three semantic relations (synonym, ‘isa’ and ‘part-of’). Its most recent version 2.1 contains roughly 200,000 word ‘senses’.
As a simple semantic network with words at the nodes, it can be readily applied to any
textual input for query expansion, or determining semantic similarity.
Source: http://wordnet.princeton.edu
2. Cyc
A knowledge base formalising commonsense knowledge into a logical framework.
Assertions are largely handcrafted by knowledge engineers at Cycorp. A free version
available for research purposes today contains more than 300,000 concepts, nearly
3,000,000 assertions (facts and rules), using more than 26,000 relations. Cyc also
provides a natural language query tool.
Source: http://research.cyc.com
According to Liu & Sing (2004) it is necessary in order to use Cyc for reasoning about
text to first map the text into its proprietary logical representation using Cyc’s own
language CycL. This mapping is reported to be quite complex because all inherent
ambiguity in natural language must be resolved to produce the unambiguous logical
formulation required by CycL. At the time of writing it is unclear whether these
difficulties are eliminated with the use of Cyc’s natural language query tool.
3. ConceptNet
A large-scale semantic network of commonsense knowledge containing over 300,000
nodes connected by more than 1.6 million relation edges using 20 different relation
types. ConceptNet consists of machine-readable logical predicates of the form: [IsA
“tennis” “sport”] and [EventForGoalEvent “play tennis” “have racket”]. It contains
everyday knowledge about the world, while WordNet follows a more formal and
taxonomic structure and permits contextual reasoning. ConceptNet has extended
WordNet’s notion of a node in the semantic network from purely lexical items to
include higher-order compound concepts as well. In particular, ConceptNet is reported
to be able to perform query-initiated contextual disambiguation.
Integrating Cross-Modal Context for Syntactic Disambiguation
9
Research Proposal
Patrick McCrae
___________________________________________________________________________
4. Research Focus
The following section describes the thematic core of the research project in more detail and
outlines the proposed approach for our investigation.
As illustrated by (S1) in section 0 above with-PP attachment can introduce on kind of
structural ambiguity. To afford a unique and contextually correct structural parse this
ambiguity needs to be resolved. In cases where the provided linguistic information alone is
insufficient to permit structural disambiguation extra-sentential knowledge and maybe even
extra-linguistic knowledge such as context or world knowledge are needed to be able to
determine the syntactic structure conclusively.
In our research project we will focus on structural ambiguity of German sentences. Initially,
we will focus on sentences containing mit (‘with’)-PPs which semantically display the same
INSTRUMENT/COMITATIVE ambiguity observed in (S1) above. For our syntactic analysis we
will employ cdg, a constraint-based syntax parser developed by the NATS group at the
University of Hamburg’s Department of Informatics under the direction of Wolfgang Menzel.
Based on a full-form lexicon and a weighted constraint grammar, cdg produces labelled
dependency trees analogous to those in (D1.1) and (D1.2). Parsing in cdg is a three-step
process:
1. Inputting the sentence to be parsed produces a cdg word graph.
2. From the word graph cdg generates all dependency edges that do not violate the
grammar’s unary constraints to produce the corresponding word net.
3. cdg applies all binary constraints to the word net and searches for the solution with the
best score to produce a ranked set of parses.
For PP attachment disambiguation cdg currently integrates an external predictor component
with the following properties:
a. Based on attachment experience from large text corpora the PP Attacher assigns
scores for the attachment of a preposition to other words in the same sentence.
b. Attachment scores are calculated for all verbs in the sentence as well for all nouns left
of the preposition.
c. Due to a limitation of the current cdg implementation the PP Attacher takes input at
token level only. Its input consists of the input sentence in the form of a string of
words and does not contain any higher syntactic information.3
d. For large texts the PP Attacher attains an accuracy of 92% (Foth & Menzel 2006a).
These properties have the following implications:
a. above implies that the scores returned by the predictor are constant for a given prepositionattachment word pair. This means we can expect only one static decision for in the
disambiguation of (S1)-like sentences regardless of context. It also means that attachment
3
Note, however, that the PP Attacher can be combined with other external components capable of making
predictions on higher syntactic properties such as phrase structure and use their input to have access to such
more complex information which cdg cannot yet provide at this stage in the parsing process.
Integrating Cross-Modal Context for Syntactic Disambiguation
10
Research Proposal
Patrick McCrae
___________________________________________________________________________
probabilities are based on the preposition alone. A contribution to PP meaning originating
from the PP kernel noun is ignored.
b. above implies that the PP Attacher evaluates attachment at word- and not at phrase-level.
c. above implies that the current PP Attacher component is ignorant of sentence structure as
well as supra-sentential context.
While d. attests unprecedented disambiguation accuracies, properties a., b. and c. lead us to
believe that the PP Attacher will not perform as well under the conditions for purely contextdriven conditions. For these cases we need to improve the cdg syntax parser’s PP attachment
disambiguation heuristics. Three generic approaches are conceivable:
1. Improve the existing PP Attacher without Context Integration
The component currently in use assigns scores for the likelihood of a preposition
attaching to another word in the sentence. As a result, we would not expect different
disambiguation decisions for (S1) and (S2). To refine PP attachment in (S1) and (S2)
we also need to consider the PP filler noun – telescope or hat.
2. Shallow Integration: Integrate Context Knowledge via a Predictor
Integrate context and world knowledge into cdg by developing a new cdg-external
predictor component capable of integrating context knowledge. This approach appeals
as it provides context sensitivity by accessing externally represented context
knowledge via a predictor. At the same token, it is therefore subject to the limitations
that all predictor integrations in cdg suffers from: predictions can only be made based
on the word graph as input, i. e. based on a word-level sentence representation which
contains no higher syntactic information.
3. Deep Integration: Integrate Context Knowledge into cdg’s Architecture
Integrate context and world knowledge by changing the internal architecture of cdg.
The core of this approach is to include a plausibility check as a separate assessment
component into the cdg architecture to assess the validity of a proposed structure in
parallel to the grammatical check. Essentially, plausibility is thus raised to the same
level as grammaticality in an architectural sense as well. While this approach could
overcome cdg’s limitations on context integration by predictor this approach would
require substantial re-engineering of the existing solution as well as extensive
development effort.
Approach 1 makes its attachment decision based on a comparison of the linguistic input
material with corpus data. Even if a perfect heuristic for obtaining an attachment probability
for a given PP to a potential attachment point could be implemented, this approach would be
blind to the kind of contextual decisions required for linguistically undecidable cases.
A key challenge of this approach would be to design a robust heuristic for determining the PP
filler noun without relying on higher syntactic knowledge. Simple heuristics such as ‘pick the
first noun within n positions after the preposition would be easy to implement but would fail
for German sentences such as (S3) in which the actual PP filler noun Tisch ‘table’ is not the
first noun to follow the preposition.
(S3)
Der Apfel lag auf dem im Dunkel gelegenen Tisch.
‘The apple lay on the table situated in the dark.’
Integrating Cross-Modal Context for Syntactic Disambiguation
11
Research Proposal
Patrick McCrae
___________________________________________________________________________
Approach 3, Deep Integration, is out of scope for its re-engineering and development effort
which would exceed the time frame and scope of this investigation by far. It may, however,
present an interesting line of approach for future research.
Approach 2, Shallow Integration, is still subject to cdg’s limitations on integrating external
predictors. It does, however, permit the integration of context knowledge without major reengineering or development in cdg itself. In pursuing Shallow Integration one of the
challenges will be to devise a predictor that can still make accurate plausibility predictions for
PP attachment even without having higher syntactic knowledge at hand – at least not from
cdg. It is hypothesised that for purely context-driven cases such as (S1) the gain in decision
accuracy for PP attachment resulting from context integration will outweigh the limitations
imposed by cdg’s requirement of applying the predictor at token level only. We therefore opt
to pursue Shallow Integration.
To achieve Shallow Integration, we need to find a suitable way to represent extra-linguistic
knowledge such as context and world knowledge for integration into the parsing process. The
integration of extra-linguistic knowledge into the parsing process will be achieved by addition
of suitable constraints to the grammar. The challenge here will be to define the integration
constraints such that outside of the modelled domain parsing accuracy is not degraded.
As for context representation, the most suitable form will still need to be evaluated. We
currently believe that context can be represented in the form of a machine-readable context
ontology which, for the modelled domain, is to contain a representation of all relevant objects
and their syntactically significant relationships to each other.
Note that the representation of this extra-linguistic knowledge does not need to be exhaustive
as long as it is sufficient to support structural disambiguation. Also, its nature and origin are
arbitrary. The context ontology can therefore be seen a unifying interface for a potentially
large variety of context data. A practical application from the field of robotics could be the
inclusion of context data integrated from cross-modal robot sensor data.
As for the scope of the context model, modelling contextual knowledge without domain
restriction would clearly be an impossible task to handle, equivalent to modelling the
knowledge of an entire human brain – in fact the union over the knowledge of all human
brains, which clearly would be beyond the scope of our investigation. For the purpose of this
research project we will therefore need to decide on a specific model domain. This domain is
yet to be selected and needs to meet the following criteria:
ƒ
The domain can be modelled with a manageable number of verbs.
ƒ
The domain can be modelled with a manageable number of nouns.
ƒ
The domain ontology contains a manageable number of domain objects.
ƒ
The domain ontology contains a manageable number of relations between domain
objects.
In summary, this section has provided a detailed rationale for the context-sensitive extension
of purely statistical PP attachment disambiguation. We have also provided arguments for
pursuing the approach of Shallow Context Integration to support syntactic disambiguation in
cdg. Finally, we have outlined how a context model can serve as an integrating interface for a
variety of cross-modal context data and have provided a list of criteria to be considered in the
selection of the model domain.
Integrating Cross-Modal Context for Syntactic Disambiguation
12
Research Proposal
Patrick McCrae
___________________________________________________________________________
5. Method
This section addresses the empirical approach for our investigation in detail. It outlines how
the instruments, materials, setup and experimental procedure will be used to answer the core
question of whether the integration of extra-linguistic knowledge such as context and world
knowledge can help improve the accuracy of structural disambiguation in syntactic parsing.
5.1.
Instruments
Parsing will be performed with the constraint-based cdg syntax parser version 1.105. The
parser runs in a LINUX V2.6.18.3wk environment with a Debian V4.0 installation.
The grammar and lexicon used are the German standard files for cdg as developed by the
NATS group at the University of Hamburg’s Department of Informatics. The files implement
the grammatical model for German as reported in Foth (2006b).
The plausibility predictor component (PPC) will be a custom-developed Java programme.
The Reasoning Component (RC) to be used for extracting relevant context information from
the context model is yet to be evaluated. It is also to be evaluated whether the context model
will be represented as an ontology.
5.2.
Material
Object of study will be German language sentences each constructed to contain structural
ambiguity which cannot be resolved on linguistic grounds alone. To begin with we will focus
on sentences containing mit-PPs. For each one of the sentences under investigation two
distinct contexts will be defined, each licensing a different structural disambiguation.
Context knowledge pertaining to the constructed sentences will be modelled manually and
represented in the context model. The model scope will be adjusted such as to contain all
information needed for a conclusive structural disambiguation decision.
5.3.
Setup
The bridge between the context model and syntactic parsing will be provided by thematic role
assignment. Due to internal restrictions of cdg a separate level of analysis is required for each
thematic role. The existing standard grammar will therefore be extended to permit thematic
role assignment for every role required. It remains to be evaluated how many thematic role
levels will need to be included to facilitate accurate thematic role assignment for a given
structural ambiguity (e. g.: assigning the thematic role INSTRUMENT might be facilitated by
already having assigned the role AGENT). The standard cdg grammar will then be extended to
include the thematic role assignment constraints. Initially, weightings for these constraints
will be set to maximum hardness, i. e.: they will contribute maximally to the sentence’s
overall grammaticality score. As part of the investigation the effects of constraint relaxation
will be considered once the behaviour of the fully integrated system has been studied for hard
constraints. Context integration will be achieved by making the semantic constraints process
disambiguation predictions as received from the context-sensitive PPC.
The existing standard lexicon will be extended to include thematic role selection criteria for
all verbs within the domain modelling scope.
Integrating Cross-Modal Context for Syntactic Disambiguation
13
Research Proposal
Patrick McCrae
___________________________________________________________________________
The context model will be edited manually. If the evaluation for representing the context
model is in favour of an ontology we plan to use the Protégé Ontology Editor Release 3.2 as
obtained from http://protege.stanford.edu to create the context model.
Depending on the outcome of the reasoning component evaluation we will either integrate an
existing, external reasoning component to extract context information from the context model
or we will extend the PPC to collect that information from the Context Model directly. The
aforementioned components will integrate into the target solution architecture (TSA) shown
in Fig 1.
Context
Model
Extended
German
Standard
Lexicon
Requests plausibility scores
f Extracts context
d
Extended
German
Standard
Grammar
Sentence
Containing
structural
ambiguity
Load into
cdg
c
cdg Parser
h
e
Plausibility Sends query
Predictor
Component
Returns context
(PPC)
knowledge
g
information
Reasoning
Component (RC)
Returns plausibility scores
Fig. 1: The proposed target solution architecture (TAS)
Context integration is proposed to be achieved along to the following sequence of steps:
1. The extended German Standard Grammar and Lexicon as well as the input sentence
containing structural ambiguity are loaded into cdg.
2. In building the corresponding word net (cf. section 4) cdg requests plausibility scores for
structural disambiguation from the PPC.
3. Based on the input parameters received from cdg, the PPC formulates a query and then
submits it to the RC.
4. The RC extracts context information from the context ontology which defines the
sentence context to be considered. It also performs reasoning on that context information
to produce context knowledge.4
5. The RC returns context knowledge to the PPC.
6. Based on the context knowledge received from the RC the PPC assigns plausibility scores
and returns them to cdg for evaluation via the constraints in the extended German
Standard Grammar.
5.4.
Experimental Procedure
In our study of structural ambiguity in German we will initially focus on PP attachment for
mit-PPs. Time permitting, we will also investigate the impact of context and world knowledge
for other cases of syntactic ambiguity.
4
Note that if the recommendation from system evaluation is not to integrate an existing reasoning component,
the PPC’s capabilities may need to be enhanced to be able to perform step 4 as well. In that case, step 5 will be
omitted.
Integrating Cross-Modal Context for Syntactic Disambiguation
14
Research Proposal
Patrick McCrae
___________________________________________________________________________
Structurally ambiguous sentences will be parsed in three different variations for each of the
two contexts:
1. Using only the current statistical PP Attacher.
2. Using only the PPC in the TSA.
3. Using both the current PP Attacher and the PPC in the TSA with equal weights.5
In total each ambiguous sentence will, therefore, be parsed six times. Parsing accuracy will be
evaluated for every sentence and context under all three conditions.
The predicted outcomes are:
1. As the current PP Attacher does incorporate the contextual information, its PP
attachment accuracy should be approximately 50% corresponding to pure chance for
changing contexts.
2. Integrating contextual knowledge into the attachment decision should improve parsing
accuracy significantly and substantially. Quantitatively, the outcome will be
determined by the quality of the ontological context model.
3. No or only marginal improvement over condition 2 is expected since the old PP
Attacher is believed to have no effect upon the accuracy of attachment decisions for
changing contexts. It may, however, effect improvement in cases which are not purely
context driven.
5
Note that condition 3 requires the integration of an additional constraint into the grammar to assign weights to
the two predictor components and handle potentially arising conflicts between their predictions. Conditions 1
and 2 can be considered special cases of condition 3 in which the weight of the other predictor component has
been set such that the other component’s prediction is completely ignored.
Integrating Cross-Modal Context for Syntactic Disambiguation
15
Research Proposal
Patrick McCrae
___________________________________________________________________________
6. Work Plan
This section presents the high-level time line for the proposed research and lists key mile
stones for every task block from the project plan.
The proposed project is structured into four phases which are made up of two activity blocks
each (Fig. 2). Note that for formatting reasons the time line is not to scale. Key mile stones
are listed below.
I. Analysis
Project
Definition
Aug 06
II. Implementation
Solution
Specification
Jan 07
Oct 07
Build
Test
Apr 08
III. Investigation
In-Depth
Research
Aug 08
Evaluation
Oct 08
IV. Documentation
Write-Up
Nov 08
Examination
May 09
Fig. 2: Project phases and task blocks on a high-level time line
I. Analysis Phase
Start: August 2006
End: October 2007
Project Definition
Nov 2006
State of the art overview completed
An initial overview over the current state of the art on context
integration in sentence processing and syntactic parsing will have been
obtained.
Jan 2007
Initial project definition completed
A functional proof of concept demonstrating the integration of external
context into a grammar will be up and running. The research topic and
scope will have been documented fully in a ten-page research proposal.
Solution Specification
Mar 2007
Solution requirements documentation completed
A detailed documentation of what the requirements for the target
system are will have been completed. This includes all evaluations of
which systems are to be used in the TSA.
Integrating Cross-Modal Context for Syntactic Disambiguation
16
Research Proposal
Patrick McCrae
___________________________________________________________________________
May 2007
Solution specification completed
A detailed documentation of how the system is to be built will have
been created. This includes a detailed specification of all system
configurations required for integration into the TSA.
Oct 2007
Research project definition refined
A well-defined research approach and methodology will have been laid
out in the form of a detailed 80-page project description.
II. Implementation Phase
Start: October 2007
End: August 2008
Build
Apr 2008
Component building completed
Development on all components to be used in the TSA will be
completed. This comprises the cdg Lexicon, the cdg Grammar, the PPC,
the RC and the Context Model.
Test
Aug 2008
Integration testing completed
Integration of all components in the target architecture will have been
tested and fixed. The TSA will be fully operational and all research
experiments will have been conducted.
III. Investigation Phase
Start: August 2008
End: November 2008
In-Depth Research
Oct 2008
All experimental findings documented
Experimental work will have been conducted and its outcomes will
have been documented in detail. Note that the observations made in this
phase may result in a feedback loop into Solution Specification to effect
modification and/or improvement of one or more solution components.
This feedback loop is shown by the dashed arrow in Fig. 3.
Evaluation
Nov 2009
All experimental outcomes evaluated
A detailed analysis of all experimental data will have been performed.
Integrating Cross-Modal Context for Syntactic Disambiguation
17
Research Proposal
Patrick McCrae
___________________________________________________________________________
IV. Documentation Phase
Start: November 2008
End: August 2009
Write-Up
Apr 2009
Thesis draft submitted for internal review
The thesis will have been submitted for internal review to Prof.
Wolfgang Menzel, the project supervisor.
May 2009
Final thesis submitted to examiners
The thesis will have been submitted to the examiners in its final form.
Examination
Aug 2009
Viva
The oral examination will have been sat.
Integrating Cross-Modal Context for Syntactic Disambiguation
18
Research Proposal
Patrick McCrae
___________________________________________________________________________
7. References
Allen, James (1995). Toward Efficient Parsing. In Natural language understanding (Chapter
6). New York: University of Rochester.
Auble, Pamela & Franks, Jeffery J. (1983). Sentence comprehension processes. Journal of
Verbal Learning and Verbal Behavior (22), 395 – 405.
Calvo, Hiram & Gelbukh, Alexander (2004). Acquiring Selectional Preferences from
Untagged Text for Prepositional Phrase Attachment Disambiguation. In F. Meziane, E.
Metais (Eds.), Natural language processing and information systems (NLDB-2004).
Lecture Notes in Computer Science, N 3136, pp. 207–216. New York: Springer.
Chwilla, Dorothee J. & Kolk, Herman H.J. (2005). Accessing World Knowledge Evidence
from N400 and Reaction Time Priming. Cognitive Brain Research (25), 589 – 606.
Crain, Stephen & Steedman, Mark (1985). On not Being Led up the Garden Path - the Use of
Context by the Psychological Syntax Parser. In D. Dowty, L. Karttunen and A. Zwicky
(Eds.), Natural language parsing: Psychological, computational, and theoretical
perspectives (pp. 320 – 358). Cambridge University Press.
Dowty, David (1989). On the Semantic Content of the Notion of 'Thematic Role'. In Gennaro
Chierchia, Barbara H. Partee and Raymond Turner (Eds.), Properties, types and
meaning (Volume II: Semantic issues). Dordrecht: Kluwer Academic Publishers, pp. 69130.
Foth, Kilian & Menzel, Wolfgang (2006a). The Benefit of Stochastic PP Attachment to a
Rule-Based Parser. In Proceedings of the 21st International Conference on
Computational Linguistics. Sydney: Coling-ACL-2006.
Foth, Kilian (2006b). Eine umfassende Dependenzgrammatik des Deutschen.
Grimshaw, Jane B. (1990). Argument structure. Cambridge MA: MIT Press.
Knoeferle, Pia S. (2005). The Role of Visual Scenes in Spoken Language Comprehension:
Evidence from Eye-Tracking (PhD Thesis). Saarbrücken: Universität des Saarlandes.
Kudo, Taku & Matsumoto, Yuji (2000). Use of Support Vector Learning for Chunk
Identification. In Proceedings of CoNLL-2000 and LLL-2000. Lisbon, Portugal.
Kuperberg, Gina R., Kreher, Donna A., Sitnikova, Tatiana, Caplan, David N., & Holcomb,
Phillip J. (2006). The Role of Animacy and Thematic Relationships in Processing
Active English Sentences: Evidence from Event-Related Potentials. In Brain and
Language, in press.
Integrating Cross-Modal Context for Syntactic Disambiguation
19
Research Proposal
Patrick McCrae
___________________________________________________________________________
Lieberman, Henry, Faaborg, Alexander, Daher, Waseem, & Espinosa, José (2005). How to
Wreck a Nice Beach You Sing Calm Incense. International Conference on Intelligent
User Interfaces, IUI 2005, January 9-12, 2005.
Liu, Hugo & Singh, Push (2004). ConceptNet – a practical commonsense reasoning tool-kit.
BT Technology Journal (22: 4), (2004: October) pp. 211-226.
Lüdtke, Dirk & Sato, Satoshi (2003). Fast Base NP Chunking with Decision Trees –
Experiments on Different POS Tag Settings. In A. Gelbukh (Ed.) Computational
linguistics and intelligent text processing, Springer LNCS, 2003, pp. 136-147.
McCawley, J. D. (1968). The Role of Semantics in a Grammar. In E. Bach & R. T. Harms
(Eds.), Universals in linguistic theory. New York: Holt, Rinehart, & Winston.
Mylne, Tom (1999). A Feature-Based System for Classifying Semantic Roles. In John
Henderson (Ed.) Proceedings of the 1999 Conference of the Australian Linguistic
Society. http://www.linguistics.uwa.edu.au/research/als99/proceedings.
Rayner, Keith, Carlson, Marcia, & Frazier, Lyn (1983). Syntax and Semantics Interaction
during Sentence Processing. Journal of Verbal Learning and Verbal Behavior (22), 358
– 374.
Simpson, Jane (1991). Warlpiri morpho-syntax: A lexicalist approach. Dordrecht: Kluwer
Academic Publishers.
Taraban, Roman & McClelland, James L. (1988). Constituent Attachment and Thematic Role
Assignment in Sentence Processing: Influences of Content-Based Expectations. Journal
of Memory and Language (27:6), (1988: December) pp. 597 – 632.
Trueswell, John C., Tanenhaus, Michael K., & Garnsey, Susan M. (1994). Semantic
Influences on Parsing: Use of Thematic Role Information in Syntactic Ambiguity
Resolution. Journal of Memory and Language (33), pp. 285 – 318.
Watanabe, Katsumi (2001). Cross-modal Interactions in Humans (PhD Thesis). Pasadena,
California: California Institute of Technology.
Integrating Cross-Modal Context for Syntactic Disambiguation
20