Learning-Based Argument Structure Analysis of Event-Nouns in Japanese

Mamoru Komachi, Ryu Iida, Kentaro Inui and Yuji Matsumoto

Graduate School of Information Science

Nara Institute of Science and Technology, JAPAN

19 September 2007

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Our goal

Our city, destroyed by the atomic bombOur city was destroyed by the atomic bombThe atomic bomb destroyed our citythe destruction of our city by the atomic bomb

IE, MT, Summarization, …

destroy

The atomic bomb Our cityNominalization

CAUSE UNDERGOER

Argument structure of event-nouns

Logical cases for event-nouns are often not marked by case markers

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Kanojo-kara denwa-ga ki-taShe-ABL phone-NOM come-PAST

(She phoned me.)

phone

she (me)

come

phone she

ABLNOM NOM DAT

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Task setting

Tom-ga kinou denwa-o ka-ttaTom-NOM yesterday phone-ACC buy-PAST

(Tom bought a phone yesterday.)

1. Event classification (determine event-hood)

2. Argument identification

buy

Tom phone

NOM ACC

phone

?

?

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Outline

IntroductionArgument structure analysis of event-nouns

Event classificationArgument identification

ConclusionFuture work

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Unsupervised learning of patterns

Encode an instance in a tree and learn contextual patterns as sub-trees by Boosting algorithm called BACT (Kudo and Matsumoto, 2004)

…persuasiondestruction…

…chairdesk…

… conducted destruction of documents …

… a little chair around…

Positive

Negative

Verb Commonnoun

Samephrase

Adj

Samephrase

Prep

Having eventhood

Not having eventhood

Encode each instance in a flat treeUsing surface text, POS, dependency relations, etc.

Depends

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Experiments of event classificationMethod: Classify eventhood of event-nouns by

Support Vector MachinesData: 80 news articles (800 sentences)

1,237 event-nouns (590 have eventhood)

Features:Grammatical features

HeadPOS: CommonNounSemantic features

SemanticCategory: AnimateContextual features

FollowsVerbalNoun: 1

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Results of event classificationPrec. Rec. F

Baseline (predominant) 60.4 88.2 71.7

Proposed (unsupervised) 73.3 80.2 76.6

Baseline: use the first sense determined by corpus statistics (NAIST Text Corpus)

Proposed: machine learning based classifierPrecision = correct / event-nouns which are classified

as having event-hood by systemRecall: correct / all event-nouns in the corpus

Outperform in precision and F by using contextual patternsCan improve more by adding more data

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Outline

IntroductionArgument structure analysis of event-nouns

Event classificationArgument identification

ConclusionFuture work

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Argument identificationBuild a classifier using tournament model (Iida et al., 2006)

政府 , 民間

政府 , 活性

日本 , 政府

日本 政府 による 民間 支援 が 活性 化 した。Japanese government-BY private sector support-NOM activate -PAST

training

民間 , 活性

政府 , 民間

日本 , 政府

L: 民間

L: 政府

R: 政府decoding

R

L

L

L: 民間L: 政府R: 政府 支援（する）

NOM

The support for the private sector by the Japanese government was activated.

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Calculation of PMI using pLSI

Estimate point-wise mutual information using Probabilistic Latent Semantic Indexing (Hoffman, 1999) where noun n depends on verb v through case marker c (Fujita et al., 2004)

P( v,c,n ) P( v,czZ

| z)P(n | z)P(z)

PMI( v,c ,n)logP( v,c,n )

P( v,c )P(n)

… pay for the shoes <pay,for> shoes

Dimension reduction by a hidden class z

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Case alignment dictionary

(ACCevent, oshie-ru) = DATpred→NOMevent

kare-ga kanojo-ni benkyo-o oshie-tahe-NOM her-DAT study-ACC teach-PAST

(He taught a lesson to her.)

kanojo-ga benkyo-sitaher-NOM study-PAST

(She studied.)

Case alternation

In NomBank, 20% of the arguments that occur outside NP are in support verb construction (Jiang and Ng, 2006)

(teach)

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Experiments of argument identification

Method: Apply the Japanese zero-anaphora resolution model (Iida et al., 2006) to the argument identification taskBoth tasks lack case markerEvent classification = anaphoricity

determination task

Data: 137 articles for training and 150 articles for testing (event-nouns: 722, NOM: 722, ACC: 278, DAT: 72)

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Features

Feature Example Instance

Lexical WordForm 日本Grammatical POS ProperNoun

Semantic CoocScore (PMI) < 支援（する） ,ガ >, 日本→ 2.80

Positional NPDependsOnSupportVerb

0

( 日本政府による )

日本 政府 による 民間 支援 が 活性化 した。Japanese government-BY private sector support-NOM activate-PAST

The support for the private sector by the Japanese government was activated.

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Feature NOM ACC DAT

Baseline 60.5 79.7 73.0

+SVC 64.2 78.0 71.4

+COOC 67.1 80.1 74.6

+SVC

+COOC

68.3 80.1 74.6

Accuracy of argument identification

Case alignment dictionary and co-occurrence statistics improved accuracy

SVC: support verb construction; COOC: co-occurrence

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Related work

Jiang and Ng (2006)Built maxent classifier for the NomBank

(Meyers et al., 2004) based on features for PropBank (Palmer et al., 2005)

Xue (2006)Used Chinese TB

Liu and Ng (2007)Applied Alternating Structure Optimization

(ASO) to the task of argument identification

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Conclusion

Defined argument structure analysis of event-nouns in Japanese

Proposed an unsupervised approach to learn contextual patterns of event-nouns to the event classification task

Performed argument identification using co-occurrence statistics and syntactic clues

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Future work

Explore semi-supervised approach to the event classification task

Use more lexical resources to the argument identification task