Question classification with log-linear models

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Question classification with log-linear models

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Annual ACM Conference on Research and Development in Information Retrieval archive
Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval table of contents

Seattle, Washington, USA

POSTER SESSION: Posters table of contents

Pages: 615 - 616

Year of Publication: 2006

ISBN:1-59593-369-7

Authors

Phil Blunsom	University of Melbourne, Victora, Australia
Krystle Kocik	University of Sydney, Australia
James R. Curran	University of Sydney, Australia

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval
ACM: Association for Computing Machinery

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 9, Downloads (12 Months): 68, Citation Count: 1

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ABSTRACT

Question classification has become a crucial step in modern question answering systems. Previous work has demonstrated the effectiveness of statistical machine learning approaches to this problem. This paper presents a new approach to building a question classifier using log-linear models. Evidence from a rich and diverse set of syntactic and semantic features is evaluated, as well as approaches which exploit the hierarchical structure of the question classes.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	S. Clark and J. Curran. Parsing the WSJ using CCG and log-linear models. In Proceedings of the 42nd Meeting of the ACL, pages 103--110, Barcelona, Spain, 2004.
	2	E. Hovy, L. Gerber, U. H. M. Junk, and C. Lin. Question answering in webclopedia. In Proceedings of the Ninth Text REtrieval Conference (TREC-9), page 655, 2001.
	3	K. Kocik. Question classification using maximum entropy models. Honours thesis, University of Sydney, 2004.
	4	Xin Li , Dan Roth, Learning question classifiers, Proceedings of the 19th international conference on Computational linguistics, p.1-7, August 24-September 01, 2002, Taipei, Taiwan [doi>10.3115/1072228.1072378]
	5	A. Ratnaparkhi. A maximum entropy part-of-speech tagger. In Proceedings of the Empirical Methods in Natural Language Processing Conference, 1996.