A Cross -Lingual ILP Solution to Zero Anaphora Resolution
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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 --- --- ---