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Question classification with semantic tree kernel

Published: 20 July 2008 Publication History

Abstract

Question Classification plays an important role in most Question Answering systems. In this paper, we exploit semantic features in Support Vector Machines (SVMs) for Question Classification. We propose a semantic tree kernel to incorporate semantic similarity information. A diverse set of semantic features is evaluated. Experimental results show that SVMs with semantic features, especially semantic classes, can significantly outperform the state-of-the-art systems.

References

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M. Collins and N. Duffy. Convolution Kernels for Natural Language. In Proceedings of Neural Information Processing Systems (NIPS14), 2001
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X. Li and D. Roth. Learning Question Classifiers. In Proceedings of the 19th International Conference on Computational Linguistics (COLING'02), 2002.
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D. Zhang and W. Lee. Question Classification Using Support Vector Machines. In Proceedings of the 26th ACM SIGIR (SIGIR'03). 2003.
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X. Li and D. Roth. Learning question classifiers: the role of semantic information. Journal of Natural Language Engineering, 12(3), 229--249, 2005.
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A. Moschitti, S. Quarteroni, R. Basili and S. Manandhar, Exploiting Syntactic and Shallow Semantic Kernels for Question/Answer Classification. In Proceedings of the 45th Conference of the Association for Computational Linguistics (ACL'07), 2007.
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P. Blunsom, K. Kocik and J. R. Curran. Question Classification with Log-Linear Models. In Proceedings of the 29th ACM SIGIR (SIGIR'06). 2006.

Cited By

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  • (2018)Capsule-Based Bidirectional Gated Recurrent Unit Networks for Question Target ClassificationInformation Retrieval10.1007/978-3-030-01012-6_6(67-77)Online publication date: 19-Sep-2018
  • (2016)Tree Similarity Measurement for Classifying Questions by Syntactic StructuresIntelligent Computing Methodologies10.1007/978-3-319-42297-8_36(379-390)Online publication date: 12-Jul-2016
  • (2013)Minimally supervised question classification on fine-grained taxonomiesKnowledge and Information Systems10.1007/s10115-012-0557-y36:2(303-334)Online publication date: 1-Aug-2013
  • Show More Cited By

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    cover image ACM Conferences
    SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
    July 2008
    934 pages
    ISBN:9781605581644
    DOI:10.1145/1390334
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    New York, NY, United States

    Publication History

    Published: 20 July 2008

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    Author Tags

    1. machine learning
    2. question answering
    3. question classification
    4. semantic class
    5. support vector machines
    6. tree kernel

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    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    Cited By

    View all
    • (2018)Capsule-Based Bidirectional Gated Recurrent Unit Networks for Question Target ClassificationInformation Retrieval10.1007/978-3-030-01012-6_6(67-77)Online publication date: 19-Sep-2018
    • (2016)Tree Similarity Measurement for Classifying Questions by Syntactic StructuresIntelligent Computing Methodologies10.1007/978-3-319-42297-8_36(379-390)Online publication date: 12-Jul-2016
    • (2013)Minimally supervised question classification on fine-grained taxonomiesKnowledge and Information Systems10.1007/s10115-012-0557-y36:2(303-334)Online publication date: 1-Aug-2013
    • (2011)Question classification by weighted combination of lexical, syntactic and semantic featuresProceedings of the 14th international conference on Text, speech and dialogue10.5555/2040037.2040070(243-250)Online publication date: 1-Sep-2011
    • (2011)From symbolic to sub-symbolic information in question classificationArtificial Intelligence Review10.1007/s10462-010-9188-435:2(137-154)Online publication date: 1-Feb-2011
    • (2011)Question Classification by Weighted Combination of Lexical, Syntactic and Semantic FeaturesText, Speech and Dialogue10.1007/978-3-642-23538-2_31(243-250)Online publication date: 2011

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