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Learning question classifiers

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Dan RothAuthors Info & Claims

COLING '02: Proceedings of the 19th international conference on Computational linguistics - Volume 1

Pages 1 - 7

https://doi.org/10.3115/1072228.1072378

Published: 24 August 2002 Publication History

Abstract

In order to respond correctly to a free form factual question given a large collection of texts, one needs to understand the question to a level that allows determining some of the constraints the question imposes on a possible answer. These constraints may include a semantic classification of the sought after answer and may even suggest using different strategies when looking for and verifying a candidate answer.This paper presents a machine learning approach to question classification. We learn a hierarchical classifier that is guided by a layered semantic hierarchy of answer types, and eventually classifies questions into fine-grained classes. We show accurate results on a large collection of free-form questions used in TREC 10.

References

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M. Light, G. Mann, E. Riloff, and E. Breck. 2001. Analyses for Elucidating Current Question Answering Technology. Journal for Natural Language Engineering. forthcoming.]]

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D. Roth, G. Kao, X. Li, R. Nagarajan, V. Punyakanok, N. Rizzolo, W. Yih, C. O. Alm, and L. G. Moran. 2002. Learning components for a question answering system. In TREC-2001.]]

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Learning question classifiers
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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COLING '02: Proceedings of the 19th international conference on Computational linguistics - Volume 1

August 2002

1184 pages

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Association for Computational Linguistics

United States

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Published: 24 August 2002

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https://dl.acm.org/doi/10.5555/3702676.3702863
Feng STramèr FSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Privacy backdoorsProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692605(13326-13364)Online publication date: 21-Jul-2024
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Chadha KJagielski MPapernot NChoquette-Choo CNasr MSalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Auditing private predictionProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3692304(6066-6092)Online publication date: 21-Jul-2024
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