Feature Selection Using a Semantic Hierarchy for Event Recognition and Type Classification

6th International Joint Conference on Natural Language Processing, IJCNLP 2013 - Proceedings of the Main Conference

Volumn , Issue , 2013, Pages 136-144

  Jeong, Yoonjae ^a   Myaeng, Sung Hyon ^a  

^a Korea Advanced Institute of Science and Technology (KAIST)   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

CHARACTER RECOGNITION; CLASSIFICATION (OF INFORMATION); FEATURE EXTRACTION; SEMANTICS; SYNTACTICS; TEXT PROCESSING;

EVENT RECOGNITION; EVENT TYPES; FEATURE SELECTION METHODS; FEATURE SPACE; FEATURES SELECTION; SEMANTIC HIERARCHIES; SYNTACTIC DEPENDENCIES; TEXT-MINING; TYPE CLASSIFICATIONS; WORDNET;

ONTOLOGY;

EID: 84924958134     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Bethard, S., & Martin, J. H. (2006). Identification of event mentions and their semantic class. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (pp. 146–154). Association for Computational Linguistics.
2. Boguraev, B., & Ando, R. (2007). Effective Use of TimeBank for TimeML Analysis. In F. Schilder, G. Katz, & J. Pustejovsky (Eds.), Annotating, Extracting and Reasoning about Time and Events (Vol. 4795, pp. 41–58). Springer Berlin Heidelberg.
3. Daniel, N., Radev, D., & Allison, T. (2003). Sub-event based multi-document summarization. In Proceedings of the HLT-NAACL 03 on Text summarization workshop (Vol. 5, pp. 9–16). Association for Computational Linguistics.
4. Jeong, Y., & Myaeng, S.-H. (2013). Using WordNet Hypernyms and Dependency Features for Phrasal-level Event Recognition and Type Classification. In P. Serdyukov, P. Braslavski, S. Kuznetsov, J. Kamps, S. Rüger, E. Agichtein, … E. Yilmaz (Eds.), Advances in Information Retrieval (Vol. 7814, pp. 267–278). Springer Berlin Heidelberg.
5. Klein, D., & Manning, C. D. (2003). Accurate unlexicalized parsing. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics (pp. 423–430). Association for Computational Linguistics.
6. Llorens, H., Saquete, E., & Navarro-Colorado, B. (2010). TimeML events recognition and classification: learning CRF models with semantic roles. In Proceedings of the 23rd International Conference on Computational Linguistics (pp. 725–733). Association for Computational Linguistics.
7. Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.
8. McCallum, A. K. (2002). MALLET: A Machine Learning for Language Toolkit.
9. Ponzetto, S. P., & Navigli, R. (2010). Knowledge-rich Word Sense Disambiguation rivaling supervised systems. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (pp. 1522–1531). Association for Computational Linguistics.
10. Pustejovsky, J. (2002). TERQAS: Time and Event Recognition for Question Answering Systems. In Proceedings of ARDA Workshop.
11. Pustejovsky, J., Castaño, J., Ingria, R., Saurí, R., Gaizauskas, R., Setzer, A., & Katz, G. (2003). TimeML: Robust Specification of Event and Temporal Expressions in Text. In Proceedings of the 5th International Workshop on Computational Semantics.
12. Pustejovsky, J., Hanks, P., Saurí, R., See, A., Gaizauskas, R., Setzer, A., … Lazo, M. (2003). The TIMEBANK Corpus. In Proceedings of the Corpus Linguistics 2003 conference (pp. 647–656).
13. Pustejovsky, J., Knippen, R., Littman, J., & Saurí, R. (2007). Temporal and Event Information in Natural Language Text. In H. Bunt, R. Muskens, L. Matthewson, Y. Sharvit, & T. E. Zimmerman (Eds.), Computing Meaning (Vol. 83, pp. 301–346). Springer Netherlands.
14. Saurí, R., Knippen, R., Verhagen, M., & Pustejovsky, J. (2005). Evita: a robust event recognizer for QA systems. In Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing (pp. 700–707). Association for Computational Linguistics.
15. Tufféry, S. (2011). Data Mining and Statistics for Decision Making (2nd ed.). John Wiley & Sons.
16. Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.). San Francisco, CA, USA: Morgan Kaufmann.