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

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. Our goal. Our city, destroyed by the atomic bomb

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

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  1. 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

  2. Our goal • Our city, destroyed by the atomic bomb • Our citywas destroyedby the atomic bomb • The atomic bombdestroyed our city • the destruction of our cityby the atomic bomb destroy CAUSE UNDERGOER Nominalization Our city The atomic bomb IE, MT, Summarization, …

  3. Argument structure of event-nouns Kanojo-karadenwa-gaki-ta She-ABL phone-NOM come-PAST • Logical cases for event-nouns are often not marked by case markers (She phoned me.) NOM ABL NOM DAT phone come she phone (me) she

  4. Task setting Tom-ga kinou denwa-o ka-tta Tom-NOM yesterday phone-ACC buy-PAST • Event classification (determine event-hood) • Argument identification (Tom bought a phone yesterday.) phone buy ? NOM ACC ? Tom phone

  5. Outline • Introduction • Argument structure analysis of event-nouns • Event classification • Argument identification • Conclusion • Future work

  6. Unsupervised learning of patterns Positive … persuasion destruction … Same phrase • Encode an instance in a tree and learn contextual patterns as sub-trees by Boosting algorithm called BACT (Kudo and Matsumoto, 2004) … conducted destruction of documents … Depends Common noun Verb Encode each instance in a flat tree Using surface text, POS, dependency relations, etc. Having eventhood Negative … chair desk … Same phrase … a little chair around… Adj Prep Not having eventhood

  7. Experiments of event classification • Method: Classify eventhood of event-nouns by Support Vector Machines • Data: 80 news articles (800 sentences) • 1,237 event-nouns (590 have eventhood) • Features: • Grammatical features • HeadPOS: CommonNoun • Semantic features • SemanticCategory: Animate • Contextual features • FollowsVerbalNoun: 1

  8. Results of event classification • Baseline: use the first sense determined by corpus statistics (NAIST Text Corpus) • Proposed: machine learning based classifier • Precision = correct / event-nouns which are classified as having event-hood by system • Recall: correct / all event-nouns in the corpus Outperform in precision and F by using contextual patterns Can improve more by adding more data

  9. Outline • Introduction • Argument structure analysis of event-nouns • Event classification • Argument identification • Conclusion • Future work

  10. Argument identification • Build a classifier using tournament model (Iida et al., 2006) R:政府 L:政府 支援（する） NOM L:民間 日本 政府 による 民間 支援 が 活性 化 した。 Japanesegovernment-BY private sector support-NOM activate -PAST The support for the private sector by the Japanese government was activated. training decoding 日本,政府 R 民間,活性 L:民間 政府,民間 L 政府,民間 L:政府 政府,活性 L 日本,政府 R:政府

  11. Calculation of PMI using pLSI • Estimate point-wise mutual information using Probabilistic Latent Semantic Indexing (Hoffman, 1999) where noun n depends on verb v throughcase marker c (Fujita et al., 2004) Dimension reduction by a hidden class z … pay forthe shoes <pay,for> shoes

  12. Case alternation kanojo-ga benkyo-sita her-NOM study-PAST (She studied.) Case alignment dictionary In NomBank, 20% of the arguments that occur outside NP are in support verb construction (Jiang and Ng, 2006) • (ACCevent, oshie-ru) = DATpred→NOMevent kare-ga kanojo-ni benkyo-o oshie-ta he-NOM her-DAT study-ACC teach-PAST (He taught a lesson to her.) (teach)

  13. Experiments of argument identification • Method: Apply the Japanese zero-anaphora resolution model (Iida et al., 2006) to the argument identification task • Both tasks lack case marker • Event classification = anaphoricity determination task • Data: 137 articles for training and 150 articles for testing (event-nouns: 722, NOM: 722, ACC: 278, DAT: 72)

  14. Features 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.

  15. Accuracy of argument identification • Case alignment dictionary and co-occurrence statistics improved accuracy SVC: support verb construction; COOC: co-occurrence

  16. 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

  17. 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

  18. Future work • Explore semi-supervised approach to the event classification task • Use more lexical resources to the argument identification task

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