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Exploiting Syntactic Patterns as Clues in Zero-Anaphora Resolution. Ryu Iida, Kentaro Inui and Yuji Matsumoto Nara Institute of Science and Technology {ryu-i,inui,matsu}@is.naist.jp June, 20th, 2006. Zero-anaphora resolution. Zero-anaphor = a gap with an anaphoric function

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Exploiting syntactic patterns as clues in zero anaphora resolution

Exploiting Syntactic Patterns as Clues in Zero-Anaphora Resolution

Ryu Iida, Kentaro Inui and Yuji MatsumotoNara Institute of Science and Technology

{ryu-i,inui,matsu}@is.naist.jp

June, 20th, 2006


Zero anaphora resolution
Zero-anaphora resolution Resolution

  • Zero-anaphor = a gap with an anaphoric function

  • Zero-anaphora resolution becoming important in many applications

  • In Japanese, even obligatory arguments of a predicate are often omitted when they are inferable from the context

    • 45.5% nominative arguments of verbs are omitted in newspaper articles


Zero anaphora resolution cont d
Zero-anaphora resolution (cont’d) Resolution

  • Three sub-tasks:

    • Zero-pronoun detection: detect a zero-pronoun

    • Antecedent identification: identify the antecedent for a given zero-pronoun

    • Anaphoricity determination:

anaphoric zero-pronoun

antecedent

Mary-wa John-ni (φ-ga ) tabako-o yameru-youni it-ta

Mary-NOM John-DAT (φ-NOM ) smoking-OBJ quit-COMP say-PAST

[Mary asked John to quit smoking.]


Zero anaphora resolution cont d1

non-anaphoric zero-pronoun Resolution

(φ-ga) ie-ni kaeri-tai

(φ -NOM) home-DAT want to go back

[(φ=I) want to go home.]

Zero-anaphora resolution (cont’d)

  • Three sub-tasks:

    • Zero-pronoun detection: detect a zero-pronoun

    • Antecedent identification: identify antecedent from the set of candidate antecedents for a given zero-pronoun

    • Anaphoricity determination: classify whether a given zero-pronoun is anaphoric or non-anaphoric

anaphoric zero-pronoun

antecedent

Mary-wa John-ni (φ-ga ) tabako-o yameru-youni it-ta

Mary-NOM John-DAT (φ-NOM ) smoking-OBJ quit-COMP say-PAST

[Mary asked John to quit smoking.]


Previous work on anaphora resolution
Previous work on anaphora resolution Resolution

  • Research trend has been shifting from rule-based approaches (Baldwin, 95; Lappin and Leass, 94; Mitkov, 97, etc.) to empirical, or learning-based, approaches (Soon et al., 2001; Ng 04, Yang et al., 05, etc.)

    • Cost-efficient solution for achieving performance comparable to best performing rule-based systems

  • Learning-based approaches represent a problem, anaphoricity determination and antecedent identification, as a set of feature vectors and apply machine learning algorithms to them


Syntactic pattern features

Mary-wa Resolution

Mary-TOP

AntecedentJohn-niJohn-DAT

zero-pronounφ-gaφ-NOM

tabako-osmoking-OBJ

predicateyameru-youniquit-CONP

predicateit-tasay-PAST

Syntactic pattern features

  • Useful clues for both anaphoricity determination and antecedent identification


Syntactic pattern features1

Mary-wa Resolution

Mary-TOP

AntecedentJohn-niJohn-DAT

zero-pronounφ-gaφ-NOM

tabako-osmoking-OBJ

predicateyameru-youniquit-CONP

predicateit-tasay-PAST

Syntactic pattern features

  • Useful clues for both anaphoricity determination and antecedent identification

  • Questions

    • How to encode syntactic patterns as features

    • How to avoid data sparseness problem


Talk outline
Talk outline Resolution

  • Zero-anaphora resolution: Background

  • Selection-then-classification model (Iida et al., 05)

  • Proposed model

    • Represents syntactic patterns based on dependency trees

    • Uses a tree mining technique to seek useful sub-trees to solve data sparseness problem

    • Incorporates syntactic pattern features in the selection-then-classification model

  • Experiments on Japanese zero-anaphora

  • Conclusion and future work


Selection then classification model scm iida et al 05

A Resolutionfederal judge in Pittsburgh issued a temporary restraining order preventing Trans World Airlines from buying additional shares of USAir Group Inc. The order, requested in a suit filed by USAir, …

candidate anaphor

federal judge

tournament model

candidate antecedents

order

USAir Group Inc

suit

candidate anaphor

USAir

Selection-then-Classification Model(SCM) (Iida et al., 05)


Selection then classification model scm iida et al 051

federal judge Resolution

tournament model

candidate antecedents

order

USAir Group Inc

suit

candidate anaphor

USAir

Selection-then-Classification Model(SCM) (Iida et al., 05)

(Iida et al. 03)

USAir Group Inc

USAir Group Inc

USAir Group Inc

Federal judge

order

USAir

suit

candidate anaphor

candidate antecedents


Selection then classification model scm iida et al 052
Selection-then-Classification Model Resolution(SCM) (Iida et al., 05)

federal judge

tournament model

candidate antecedents

order

USAir Group Inc

USAir Group Inc

suit

most likelycandidate antecedent

candidate anaphor

USAir


Selection then classification model scm iida et al 053

USAir Group Inc Resolution

USAir

Anaphoricitydetermination model

score ≧θ

ana

scoreθ

ana

is anaphoric and

USAir

is non-anaphoric

USAir

is the

antecedent of

USAir Group Inc

USAir

Selection-then-Classification Model(SCM) (Iida et al., 05)

federal judge

tournament model

candidate antecedents

order

USAir Group Inc

USAir Group Inc

suit

most likelycandidate antecedent

candidate anaphor

USAir


Selection then classification model scm iida et al 054

USAir Group Inc Resolution

USAir

Anaphoricitydetermination model

score ≧θ

ana

scoreθ

ana

is anaphoric and

USAir

is non-anaphoric

USAir

is the

antecedent of

USAir Group Inc

USAir

Selection-then-Classification Model(SCM) (Iida et al., 05)

federal judge

tournament model

candidate antecedents

order

USAir Group Inc

USAir Group Inc

suit

most likelycandidate antecedent

candidate anaphor

USAir


Training the anaphoricity determination model

Anaphoric Resolution

Non-anaphoric

Training the anaphoricity determination model

NP1

set of candidate antecedents

NPi: candidate antecedent

NP2

NP3

Anaphoricinstances

Antecedent

NP4

NP5

NP4

ANP

anaphoric noun phrase

ANP

NP1

tournament model

NP2

set of candidate antecedents

NP3

Non-anaphoricinstances

candidate antecedent

NP4

NP3

NP5

non-anaphoric noun phrase

NP3

NANP

NANP


Talk outline1
Talk outline Resolution

  • Zero-anaphora resolution: Background

  • Selection-then-classification model (Iida et al., 05)

  • Proposed model

    • Represents syntactic patterns based on dependency trees

    • Uses a tree mining technique to seek useful sub-trees to solve data sparseness problem

    • Incorporates syntactic pattern features in the selection-then-classification model

  • Experiments on Japanese zero-anaphora

  • Conclusion and future work


New model

candidate Resolutionanaphor

USAir Group Inc

USAir

Anaphoricitydetermination model

score ≧θ

ana

scoreθ

ana

is anaphoric and

USAir

is non-anaphoric

USAir

is the

antecedent of

USAir Group Inc

USAir

New model

federal judge

tournament model

candidate antecedents

order

USAir Group Inc

USAir Group Inc

suit

most likelycandidate antecedent

USAir


Use of syntactic pattern features
Use of syntactic pattern features Resolution

  • Encoding parse tree features

  • Learning useful sub-trees


Encoding parse tree features

Mary-wa Resolution

Mary-TOP

AntecedentJohn-niJohn-DAT

zero-pronounφ-gaφ-NOM

tabako-osmoking-OBJ

predicateyameru-youniquit-CONP

predicateit-tasay-PAST

Encoding parse tree features


Encoding parse tree features1

Mary-wa Resolution

Mary-TOP

tabako-osmoking-OBJ

Encoding parse tree features

AntecedentJohn-niJohn-DAT

zero-pronounφ-gaφ-NOM

predicateyameru-youniquit-CONP

predicateit-tasay-PAST


Encoding parse tree features2

Antecedent Resolution

zero-pronoun

predicate

predicate

Encoding parse tree features

AntecedentJohn-niJohn-DAT

zero-pronounφ-gaφ-NOM

predicateyameru-youniquit-CONP

predicateit-tasay-PAST


Encoding parse tree features3

ni ResolutionDAT

gaCONJ

youniCONJ

taPAST

Encoding parse tree features

AntecedentJohn-niJohn-DAT

zero-pronounφ-gaφ-NOM

predicateyameru-youniquit-CONP

predicateit-tasay-PAST

Antecedent

zero-pronoun

predicate

predicate


Encoding parse trees

( ResolutionTL)

(TI)

LeftCand

zero-

pronoun

predicate

predicate

(TR)

LeftCand

RightCand

predicate

RightCand

zero-

pronoun

predicate

predicate

Encoding parse trees

LeftCandMary-wa

Mary-TOP

RightCand John-niJohn-DAT

zero-pronounφ-gaφ-NOM

tabako-osmoking-OBJ

predicateyameru-youniquit-CONP

predicateit-tasay-PAST


Encoding parse trees1
Encoding parse trees Resolution

  • Antecedent identification

root

Three sub-trees


Encoding parse trees2

Resolution

n

1

2

Lexical, Grammatical, Semantic, Positional and Heuristic binary features

Encoding parse trees

  • Antecedent identification

root

Three sub-trees


Encoding parse trees3

label Resolution

Left or right

Encoding parse trees

  • Antecedent identification

root

n

1

2

Lexical, Grammatical, Semantic, Positional and Heuristic binary features

Three sub-trees


Learning useful sub trees
Learning useful sub-trees Resolution

  • Kernel methods:

    • Tree kernel (Collins and Duffy, 01)

    • Hierarchical DAG kernel (Suzuki et al., 03)

    • Convolution tree kernel (Moschitti, 04)

  • Boosting-based algorithm:

    • BACT (Kudo and Matsumoto, 04) system learns a list of weighted decision stumps with the Boosting algorithm


Learning useful sub trees1

decision stumps Resolution

learn

weight

sub-tree

0.4

Label

positive

apply

Score: +0.34 positive

Learning useful sub-trees

  • Boosting-based algorithm: BACT

    • Learns a list of weighted decision stumps with Boosting

    • Classifies a given input tree by weighted voting

Training instances

positive

Labels

positive

positive

….


Overall process

score Resolutionintra≧θintra

Output the most-likely candidate antecedent appearing in S

scoreintra<θintra

scoreinter≧θinter

Inter-sentential model

Output the most-likely candidate appearing outside of S

scoreinter<θinter

Return ‘‘non-anaphoric’’

Overall process

Input (a zero-pronoun φ in the sentence S)

syntactic patterns

Intra-sentential model


Table of contents
Table of contents Resolution

  • Zero-anaphora resolution

  • Selection-then-classification model (Iida et al., 05)

  • Proposed model

    • Parse encoding

    • Tree mining

  • Experiments

  • Conclusion and future work


Experiments

# of correctly resolved zero-anaphoric relations Resolution

# of anaphoric zero-pronouns

# of correctly resolved zero-anaphoric relations

# of anaphoric zero-pronouns the model detected

Experiments

  • Japanese newspaper article corpus comprising zero-anaphoric relations: 197 texts (1,803 sentences)

    • 995 intra-sentential anaphoric zero-pronouns

    • 754 inter-sentential anaphoric zero-pronouns

    • 603 non-anaphoric zero-pronouns

  • Recall =

  • Precision =


Experimental settings
Experimental settings Resolution

  • Conducting five-fold cross validation

  • Comparison among four models

    • BM: Ng and Cardie (02)’s model:

      • Identify an antecedent with candidate-wise classification

      • Determine the anaphoricity of a given anaphor as a by-product of the search for its antecedent

    • BM_STR: BM +syntactic pattern features

    • SCM: Selection-then-classification model (Iida et al., 05)

    • SCM_STR: SCM + syntactic pattern features


Results of intra sentential zar
Results of intra-sentential ZAR Resolution

  • Antecedent identification (accuracy)

     The performance of antecedent identification improved by using syntactic pattern features


Results of intra sentential zar1
Results of intra-sentential ZAR Resolution

  • antecedent identification + anaphoricity determination


Impact on overall zar
Impact on overall ZAR Resolution

  • Evaluate the overall performance for both intra-sentential and inter-sentential ZAR

  • Baseline model:SCM

    • resolves intra-sentential and inter-sentential zero-anaphora simultaneously with no syntactic pattern features.



Auc curve
AUC curve Resolution

  • AUC (Area Under the recall-precision Curve) plotted by altering θintra

    • Not peaky  optimizing parameter θintra is not difficult


Conclusion
Conclusion Resolution

  • We have addressed the issue of how to use syntactic patterns for zero-anaphora resolution.

    • How to encode syntactic pattern features

    • How to seek useful sub-trees

  • Incorporating syntactic pattern features into our selection-then-classification model improves the accuracy for intra-sentential zero-anaphora, which consequently improves the overall performance of zero-anaphora resolution


Future work
Future work Resolution

  • How to find zero-pronouns?

    • Designing a broader framework to interact with analysis of predicate argument structure

  • How to find a globally optimal solution to the set of zero-anaphora resolution problems in a given discourse?

    • Exploring methods as discussed by McCallum and Wellner (03)


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