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On the Issue of Combining Anaphoricity Determination and Antecedent Identification in Anaphora Resolution. Ryu Iida, Kentaro Inui, Yuji Matsumoto Nara Institute of Science and Technology {ryu-i,inui,matsu}@is.naist.jp NLP-KE’05, October 30, 2005.

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On the Issue of Combining Anaphoricity Determination and Antecedent Identification in Anaphora Resolution

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On the Issue of Combining Anaphoricity Determination and Antecedent Identification in Anaphora Resolution

Ryu Iida, Kentaro Inui, Yuji Matsumoto

Nara Institute of Science and Technology

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

NLP-KE’05, October 30, 2005


Noun phrase anaphora resolution l.jpg

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, dealt another blow to TWA's bid to buy the company for $52 a share.

antecedent

anaphor

Noun phrase anaphora resolution

  • Anaphora resolution is the process of determining whether two expressions in natural language refer to the same real world entity

  • Important process for various NLP applications: machine translation, information extraction, question answering


Noun phrase anaphora resolution3 l.jpg

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, dealt another blow to TWA's bid to buy the company for $52 a share.

antecedent

anaphor

non-anaphor

Noun phrase anaphora resolution

  • Anaphora resolution can be decomposed into two sub processes

    • Anaphoricity determination is the task of classifying whether a given noun phrase (NP) is anaphoric or non-anaphoric

    • Antecedent identification is the identification of the antecedent of a given anaphoric NP


Previous work l.jpg

Previous work

  • Early corpus-based work on anaphora resolution does not address anaphoricity determination (Hobbs `78, Lappin and Leass `94)

    • Assuming that the anaphora resolution system knows a priori all the anaphoric noun phrases

  • This problem has been paid attention by an increasing number of researchers (Bean and Riloff `99, Ng and Cardie `02, Uryupina `03, Ng `04)

    • Determining anaphoricity is not a trivial problem

    • Overall performance of anaphora resolution cruciallydepends on the accuracy of anaphoricity determination


Previous work cont d l.jpg

Previous work (Cont’d)

  • Previous efforts to tackle anaphoricity determination problem have provided the two findings

    • One useful cue for determining anaphoricity of a given NP can be obtained by searching for an antecedent(Soon et al. 01, Ng and Cardie 02a)

    • Anaphoricity determination can be effectively carried out by a binary classifier that learns instances of non-anaphoric NPs (Ng and Cardie 02b, Ng 04)

  • None of the previous models effectively combinesthe strengths of these findings


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Aim

  • Improvinganaphora resolution performance:

    • Using better anaphoricity determination

    • Combining sources of evidence from previous models


Proposal l.jpg

Proposal

  • Introducing a 2-step process for combining antecedent information and non-anaphoric information

  • We call this model the selection-and-classification model

  • Select the most likely candidate antecedent (CA) of a target NP (TNP) using the tournament model (Iida et al. `03)

  • Classify a TNP paired with CA is classified asanaphoricif CA is identified as the antecedent of TNP; otherwise TNP is judgednon-anaphoric


2 step process for anaphora resolution l.jpg

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

federal judge

tournament model

candidate antecedents

order

USAir Group Inc

suit

candidate anaphor

USAir

2-step process for anaphora resolution


2 step process for anaphora resolution9 l.jpg

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

federal judge

tournament model

candidate antecedents

order

USAir Group Inc

USAir Group Inc

suit

candidate anaphor

USAir

USAir Group Inc

Federal judge

order

USAir

suit

candidate anaphor

candidate antecedents

2-step process for anaphora resolution


2 step process for anaphora resolution10 l.jpg

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

federal judge

tournament model

candidate antecedents

order

USAir Group Inc

USAir

USAir Group Inc

Anaphoricitydetermination model

suit

score θ

ana

candidate anaphor

USAir

scoreθ

ana

is anaphoric and

USAir

is non-anaphoric

USAir

is the

antecedent of

USAir Group Inc

USAir

2-step process for anaphora resolution

USAir Group Inc

candidate antecedent


Training phase l.jpg

Anaphoricinstances

NP4

ANP

tournament model

NP3

NANP

Non-anaphoricinstances

candidate antecedent

NP3

Training phase

  • Anaphoric

  • Non-anaphoric

NP1

set of candidate antecedents

NPi: candidate antecedent

NP2

NP3

Antecedent

NP4

NP5

Anaphoric NP

ANP

NP1

set of candidate antecedents

NP2

NP3

NP4

NP5

Non-anaphoric NP

NANP


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Comparison with previous approaches

  • Search-based approach (SM)(Soon et al. `01, Ng and Cardie `02)

    • Recasting anaphora resolution as binary classification problems

    • Comparable to the state-of-the-art rule-based system

    • disadvantage: not use non-anaphoric instances in training

  • Classification-and-search approach (CSM) (Ng and Cardie `02, Ng `04)

    • Introducing anaphoricity determination as a classification task

    • The performance of the CSM is better than the SMif the threshold parameters are appropriately tuned

    • disadvantage:not use the contextual information(i.e. whether an appropriate antecedent appears on the context)


Experiments l.jpg

# of correctly detected anaphoric relations

# of correctly detected anaphoric relations

# of NPs classified as anaphoric

# of anaphoric NPs

Experiments

  • Noun phrase anaphora resolution in Japanese

  • Japanese newspaper article corpus tagged NP-anaphoric relations

    • 90 text, 1,104 sentences

    • Noun phrases : 876 anaphors and 6,292 non-anaphors

      Recall =

      Precision =


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Experimental setting

  • Conduct 10-fold cross-validation with support vector machines

  • Comparison among three models

    • Search-based model (Ng and Cardie `02)

    • Classification-and-Search model (Ng and Cardie `04)

    • Selection-and-Classification model (Proposed model) using the tournament model (Iida et al. `03)


Results of noun phrase anaphora resolution l.jpg

Results of noun phrase anaphora resolution

Proposed model

Classification-and-search model

Search-based model

Search-basedmodel (SM) vs. Classification-and-search model (CSM)the performance of CSM is significantlybetter than the SM


Results of noun phrase anaphora resolution16 l.jpg

Results of noun phrase anaphora resolution

Proposed model

Classification-and-search model

Search-based model

Classification-and-search model (CSM) vs.Proposed model

the proposed model outperforms the CSMin the higher-recall portion


Conclusion l.jpg

Conclusion

  • Our selection-and-classification approach to anaphora resolution improves on the performance of previous learning-based models by combining their advantages

    • Our model uses non-anaphoric instances together with anaphoric instances to induce anaphoricity classifier

    • Our model determines the anaphoricity of a given NP by taking antecedent information into account


Future work l.jpg

Future work

  • The majority of errors are caused by the difficulty of judging the semantic compatibilitye.g.) the system outputs that “ani (elder brother)” is anaphoric with “kanojo (she)”

    • The lexical resource we employed in the experiments did not contain gender information

      Developing a lexical resource which includes a broad range of semantic compatible relations


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