A Cross-Lingual ILP Solution to Zero Anaphora ResolutionRyu Iida & Massimo Poesio (ACL-HLT 2011)

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Zero-anaphora resolution Anaphoric function in which phonetic

realization of anaphors is not required in “pro-drop” languages Based on speaker and hearer’s shared

understanding

φ: zero-anaphor (non-realized argument)

Essential: 64.3% of anaphors in Japanese newspaper articles are zeros (Iida et al. 2007)

English: John went to visit some friends. On the way, he bought some wine.Italian: Giovanni andò a far visita a degli amici. Per via, φ comprò del vino.Japanese: John-wa yujin-o houmon-sita. Tochu-de φ wain-o ka-tta.

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Research background Zero-anaphora resolution has remained an

active area for Japanese (Seki et al. 2002, Isozaki&Hirao 2003, Iida et al. 2007, Imamura et al. 2009, Sasano et al. 2009, Taira et al. 2010)

The availability of the annotated corpora such that provided by SemEVAL2010 task10 “Multi-lingual coreference (Recasens et al.2010) is leading to renewed interest (e.g. Italian) Mediocre results obtained on zero anaphors by

most systems in SemEVAL e.g. I-BART’s recall on zeros < 10%

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Resolving zero-anaphors requires

The simultaneous decision of Zero-anaphor detection: find phonetically

unrealized arguments of predicates (e.g. verbs) Antecedent identification: search for an

antecedent of a zero-anaphor Roughly correspond to anaphoricity

determination and antecedent identification in coreference resolution Denis&Baldridge(2007) proposed a solution to

optimize the outputs from anaphoricity determination and antecedent identification by using Integer Linear Programming (ILP)

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Main idea Apply Denis&Baldridge (2007)’s ILP

framework to zero-anaphora resolution Extend the ILP framework into a two-way

to make it more suitable for zero-anaphora resolution

Focus on Italian and Japanese zero-anaphora to investigate whether or not our approach is useful across languages Study only subject zero-anaphors (only

type in Italian)

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Topic of contents Research background Denis&Baldridge (2007)’s ILP model Proposal: extending the ILP model Empirical evaluations Summary & future directions

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Denis&Baldrige (2007)’s ILP formulation of base model

object function

If , mentions i and j are coreferent and mention j is an anaphor

: 1 if mentions i and j are coreferent; otherwise 0

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Denis&Baldrige (2007)’s ILP formulation of joint model

object function

If ,

mentions i and j are coreferent and mention j is an anaphor; otherwise j is non-anaphoric

: 1 if mention j is an anaphor; otherwise 0

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3 constraints in ILP modelcharacteristics of coreference

relations

transitivity of coreference chains

1. Resolve only anaphors:if mention pair ij is coreferent,mention j must be anaphoric

2. Resolve anaphors:if mention j is anaphoric, it must be coreferent with at least one antecedent

3. Do not resolve non-anaphors:if mention j is non-anaphoric, it should be have no antecedents

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Proposal: extending the ILP framework

Denis&Baldridge’s original ILP-based model is not suitable for zero-anaphora resolution

Two modifications1. Applying best-first solution 2. Incorporating a subject detection model

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1. Best-first solution Select at most one antecedent for an

anaphor “Do-not-resolve-anaphors” constraint is too

weak Allow the redundant choice of more than one

candidate antecedent Lead to decreasing precision on zero-anaphora

resolution “Do-not-resolve-anaphors” constraint is

replaced with “Best First constraint (BF)” that blocks selection of more than one antecedent:

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2. Integrating subject detection model

Zero-anaphor detection Difficulty in zero-anaphora resolution

comparing to pronominal reference resolution

Simply relying on the parser is not enough most dependency parsers are not very

accurate at identifying grammatical roles detecting subject is crucial for zero-anaphor

detection

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2. Integrating subject detection model

Resolve only non-subjects:if a predicate j syntactically depends on a subject,the predicate j should have no antecedent of its zero anaphor

: 1 if predicate j syntactically depends on a subject; otherwise 0

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Experiment 1: zero-anaphors Compare the baseline models with the

extended ILP-based models Use the Maximum Entropy model to

create base classifiers in the ILP framework and baselines

Feature definitions basically follow the previous work (Iida et al. 2007) and (Poesio et al. 2010)

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Two baseline models PAIRWISE classification model (PAIRWISE)

Antecedent identification and anaphoricity determination are simultaneously executed by a single classifier (as in Soon et al. 2001)

Anaphoricity Determination-then-Search antecedent CASCADEd model (DS-CASCADE)1. Filter out non-anaphoric candidate anaphors

using an anaphoricity determination model2. Select an antecedent from a set of candidate

antecedents of anaphoric anaphors using an antecedent identification model

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Data sets Italian (Wikipedia articles)

LiveMemories text corpus 1.2 (Rodriguez et al. 2010) Data set on the SemEval2010: Coreference

Resolution in Multiple Languages #zero-anaphors: train 1,160 / test 837

Japanese (newspaper articles) NAIST text corpus (Iida et al. 2007) ver.1.4ß

#zero-anaphors: train 29,544 / test 11,205

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Creating subject detection models Data sets

Italian: 80,878 tokens in TUT corpus (Bosco et al. 2010) Japanese: 1753 articles (i.e. training dataset) in NAIST

text corpus merged with Kyoto text corpus dependency arc is judged as positive if its relation is subject;

as negative otherwise Induce a maximum entropy classifier based on the

labeled arcs Features

Italian: lemmas, PoS tags and morphological information automatically computed by TextPro (Pianta et al. 2008)

Japanese: similar features as Italian except gender and number information

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Results for zero anaphorsItalian Japanese

model R P F R P FPAIRWISE 0.86

40.172

0.287 0.286

0.308

0.296

DS-CASCADE 0.396

0.684

0.502 0.345

0.194

0.248

ILP 0.905

0.034

0.065 0.379

0.238

0.293

ILP+BF 0.803

0.375

0.511 0.353

0.256

0.297

ILP+SUBJ 0.900

0.034

0.066 0.371

0.315

0.341

ILP+BF+SUBJ 0.777

0.398

0.526

0.345

0.348

0.346

+BF: use best first constraint, +SUBJ: use subject detection model

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Experiment 2: all anaphors Investigate performance of all anaphors (i.e.

NP- coreference and zero-anaphors) Use the same data set and same data

separation Italian: LiveMemories text corpus 1.2 Japanese: NAIST text corpus 1.4ß

Performance of each model are compared in terms of MUC score

Different types of referring expressions display very different anaphoric behavior Induce 2 different models for NP-coreference and

zero-anaphora respectively

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Italian Japanesemodel R P F R P FPAIRWISE 0.56

60.314

0.404 0.427

0.240

0.308

DS-CASCADE 0.246

0.686

0.362 0.291

0.488

0.365

I-BART (Poesio et al. 2010)

0.532

0.441

0.482 --- --- ---

ILP 0.607

0.384

0.470 0.490

0.304

0.375

ILP+BF 0.563

0.519

0.540 0.446

0.340

0.386

ILP+SUBJ 0.606

0.387

0.473 0.484

0.353

0.408

ILP+BF+SUBJ 0.559

0.536

0.547

0.441

0.415

0.427

Results for all anaphors

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Summary Extended Denis&Baldridge (2007)’s ILP-

based coreference resolution model by incorporating modified constraints & a subject detection model

Our results show the proposed model obtained improvement on both zero-anaphora resolution and overall coreference resolution

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Future directions Introduce more sophisticated antecedent

identification model Test our model for English constructions

resembling zero-anaphora Null instantiations in SEMEVAL 2010

‘Linking Events and their Participants in Discourse’ task

Detect generic zero-anaphors Have no antecedent in the preceding context e.g. the Italian and Japanese translation of

I walked into the hotel and (they) said …

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Data sets on English coreference

Use ACE-2002 data set Data set is classified into the two subset

Pronouns and NPs

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Details of experiment: Englishtraining data

train: NPs

train: zeros

models: NP

coreference

models: zero

anaphora

test data

test: NPs test: zeros

outputs: all anaphors

outputs: NPs

outputs: zeros

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Results: all anaphors (English)

Englishmodel R P FPAIRWISE 0.63

90.675

0.656

DS-CASCADE 0.597

0.597

0.597

ILP 0.736

0.380

0.501

ILP+BF 0.665

0.714

0.689

ILP+SUBJ --- --- ---ILP+BF+SUBJ --- --- ---