Using syntactic and semantic structural kernels for classifying definition questions in Jeopardy!

Alessandro Moschitti, Jennifer Chu-Carroll, Siddharth Patwardhan, James Fan, Giuseppe Riccardi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

The last decade has seen many interesting applications of Question Answering (QA) technology. The Jeopardy! quiz show is certainly one of the most fascinating, from the viewpoints of both its broad domain and the complexity of its language. In this paper, we study kernel methods applied to syntactic/semantic structures for accurate classification of Jeopardy! definition questions. Our extensive empirical analysis shows that our classification models largely improve on classifiers based on word-language models. Such classifiers are also used in the state-of-the-art QA pipeline constituting Watson, the IBM Jeopardy! system. Our experiments measuring their impact on Watson show enhancements in QA accuracy and a consequent increase in the amount of money earned in game-based evaluation.

Original languageEnglish
Title of host publicationEMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference
Pages712-724
Number of pages13
Publication statusPublished - 3 Oct 2011
Externally publishedYes
EventConference on Empirical Methods in Natural Language Processing, EMNLP 2011 - Edinburgh, United Kingdom
Duration: 27 Jul 201131 Jul 2011

Other

OtherConference on Empirical Methods in Natural Language Processing, EMNLP 2011
CountryUnited Kingdom
CityEdinburgh
Period27/7/1131/7/11

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ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Information Systems

Cite this

Moschitti, A., Chu-Carroll, J., Patwardhan, S., Fan, J., & Riccardi, G. (2011). Using syntactic and semantic structural kernels for classifying definition questions in Jeopardy! In EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference (pp. 712-724)