1 exploiting syntactic patterns as clues in zero- anaphora resolution ryu iida, kentaro inui and...

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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.jpJune, 20th, 2006

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Zero-anaphora 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

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Zero-anaphora resolution (cont’d) Three sub-tasks:

Zero-pronoun detection: detect a zero-pronoun Antecedent identification: identify the antecedent for a given z

ero-pronoun Anaphoricity determination:

Mary-wa John-ni (φ-ga ) tabako-o yameru-youni it-taMary-NOM John-DAT (φ-NOM ) smoking-OBJ quit-COMP say-PAST[Mary asked John to quit smoking.]

anaphoric zero-pronounantecedent

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Zero-anaphora resolution (cont’d) Three sub-tasks:

Zero-pronoun detection: detect a zero-pronoun Antecedent identification: identify antecedent from the set of candidate ant

ecedents for a given zero-pronoun Anaphoricity determination: classify whether a given zero-pronoun is anap

horic or non-anaphoric

(φ-ga) ie-ni kaeri-tai (φ -NOM) home-DAT want to go back [(φ=I) want to go home.]

non-anaphoric zero-pronoun

Mary-wa John-ni (φ-ga ) tabako-o yameru-youni it-taMary-NOM John-DAT (φ-NOM ) smoking-OBJ quit-COMP say-PAST[Mary asked John to quit smoking.]

anaphoric zero-pronounantecedent

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Previous work on anaphora 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 perf

orming 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

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Useful clues for both anaphoricity determination and antecedent identification

Syntactic pattern features

Mary-waMary-TOP

predicateyameru-youni

quit-CONP

zero-pronoun φ-ga

φ-NOM

predicateit-ta

say-PAST

Antecedent John-ni

John-DATtabako-o

smoking-OBJ

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Useful clues for both anaphoricity determination and antecedent identification

Questions How to encode syntactic patterns as features How to avoid data sparseness problem

Syntactic pattern features

Mary-waMary-TOP


quit-CONP

zero-pronoun φ-ga

φ-NOM

predicateit-ta

say-PAST

Antecedent John-ni

John-DATtabako-o

smoking-OBJ

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Talk outline

1. Zero-anaphora resolution: Background2. Selection-then-classification model (Iida et al., 05)3. 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

4. Experiments on Japanese zero-anaphora5. Conclusion and future work

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A federal 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, …

A federal 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

tournament model

USAir

suit

USAir Group Inc

order

federal judge

candidate anaphor

candidate antecedents …

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

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tournament model

USAir

suit

USAir Group Inc

order

federal judge

candidate anaphor

candidate antecedents …

USAir Group IncUSAir Group Inc

USAirsuitUSAir Group IncFederal judgecandidate anaphorcandidate antecedents

…order


(Iida et al. 03)

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USAir Group Inc

most likelycandidate antecedent

tournament model

USAir

suit

USAir Group Inc

order

federal judge

candidate anaphor

candidate antecedents

…


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USAir Group Inc


tournament model

USAir

suit

USAir Group Inc

order

federal judge

candidate anaphor


…is non-anaphoricUSAir

score 　 θ ana<score ≧ θ ana

is anaphoric andis the USAir

USAirUSAir Group Inc antecedent of

Anaphoricitydetermination model

USAir Group Inc USAir


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USAir Group Inc


tournament model

USAir

suit

USAir Group Inc

order

federal judge

candidate anaphor









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Anaphoric

Non-anaphoric

NANP

NP5

NP4

NP3

NP2

NP1

non-anaphoric noun phrase

set of candidate antecedents

NP3

tournament model

candidate antecedent

Non-anaphoricinstances

NP3 NANP

ANP

NP5

NP4

NP3

NP2

NP1

anaphoric noun phrase

set of candidate antecedents

Antecedent Anaphoricinstances

NP4 ANP

NPi: candidate antecedent

Training the anaphoricity determination model

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Talk outline

1. Zero-anaphora resolution: Background2. Selection-then-classification model (Iida et al., 05)3. 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

4. Experiments on Japanese zero-anaphora5. Conclusion and future work

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USAir Group Inc


tournament model

USAir

suit

USAir Group Inc

order

federal judgecandidate antecedents







New model

LeftCand predicatezero-

pronounpredicate

(TL)


pronounpredicate

(TL)

candidate anaphor

LeftCand predicateRightCand

(TI)


(TI)LeftCand predicate

zero-pronoun

predicate

(TL)


pronounpredicate

(TL)

RightCand

(TR)

predicatezero-

pronounpredicateRightCand

(TR)

predicatezero-

pronounpredicate

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Use of syntactic pattern features

Encoding parse tree features

Learning useful sub-trees

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Mary-waMary-TOP


quit-CONP

zero-pronoun φ-ga

φ-NOM

predicateit-ta

say-PAST

Antecedent John-ni

John-DATtabako-o

smoking-OBJ

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quit-CONP

zero-pronoun φ-ga

φ-NOM

predicateit-ta

say-PAST

Antecedent John-ni

John-DATMary-wa

Mary-TOPtabako-o

smoking-OBJ

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Antecedent predicatezero-pronoun predicate


quit-CONP

zero-pronoun φ-ga

φ-NOM

predicateit-ta

say-PAST

Antecedent John-ni

John-DAT

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Antecedent predicatezero-pronoun predicate

youniCONJ

niDAT

gaCONJ

taPAST


quit-CONP

zero-pronoun φ-ga

φ-NOM

predicateit-ta

say-PAST

Antecedent John-ni

John-DAT

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Encoding parse trees


(TI)LeftCand predicate

zero-pronoun

predicate

(TL)

RightCand

(TR)

predicatezero-

pronounpredicate

LeftCandMary-wa

Mary-TOP


quit-CONP

zero-pronoun φ-ga

φ-NOM

predicateit-ta

say-PAST

RightCand John-ni

John-DATtabako-o

smoking-OBJ

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Antecedent identification

root

Three sub-trees


(TI)


(TI)LeftCand predicatezero-

pronounpredicate

(TL)


pronounpredicate

(TL)

RightCand

(TR)

predicatezero-


(TR)

predicatezero-

pronounpredicate

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root

Three sub-trees

　 1 　 2 　 n……

f f f

Lexical, Grammatical, Semantic, Positional and Heuristic binary features


(TI)



pronounpredicate

(TL)


pronounpredicate

(TL)

RightCand

(TR)

predicatezero-


(TR)

predicatezero-

pronounpredicate

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root

　 1 　 2 　 n……

f f f

Three sub-trees

Lexical, Grammatical, Semantic, Positional and Heuristic binary features


(TI)



pronounpredicate

(TL)


pronounpredicate

(TL)

RightCand

(TR)

predicatezero-


(TR)

predicatezero-

pronounpredicate

Left or rightlabel

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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

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positivepositive

Boosting-based algorithm: BACT Learns a list of weighted decision stumps with Boosting Classifies a given input tree by weighted voting


positiveLabels

Training instances

….

0.4weight

Label

positive

sub-tree

decision stumpslearn

Score: +0.34 positive

apply

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Overall process

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

Intra-sentential model

Inter-sentential model

scoreintra<θintra

scoreintra≧θintra

Output the most-likely candidate antecedent

appearing in S

scoreinter≧θinter

Output the most-likely candidate appearing

outside of Sscoreinter<θinter

Return ‘‘non-anaphoric’’

syntactic patterns

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Table of contents

1. Zero-anaphora resolution2. Selection-then-classification model (Iida et al., 05)3. Proposed model

Parse encoding Tree mining

4. Experiments 5. Conclusion and future work

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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 =

Experiments

# of correctly resolved zero-anaphoric relations# of anaphoric zero-pronouns

# of anaphoric zero-pronouns the model detected

# of correctly resolved zero-anaphoric relations

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Experimental settings

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

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Results of intra-sentential ZAR

Antecedent identification (accuracy)

The performance of antecedent identification improved by using syntactic pattern features

BM (Ng02) BM_STR SCM (Iida05) SCM_STR

48.0%(478/995)

63.5%(632/995)

65.1%(648/995)

70.5%(701/995)

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antecedent identification + anaphoricity determination

Results of intra-sentential ZAR

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Impact on overall ZAR 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.

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Results of overall ZAR

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AUC curve AUC (Area Under the recall-precision Curve) plotted by

altering θintra Not peaky optimizing parameter θintra is not difficult

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Conclusion

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

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Future workHow 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)

1 exploiting syntactic patterns as clues in zero- anaphora resolution ryu iida, kentaro inui and...

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