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Learning question classifiers

Authors:

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

Publication History

Published: 24 August 2002

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Overall Acceptance Rate 1,537 of 1,537 submissions, 100%

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Cited By

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https://dl.acm.org/doi/10.1145/3678175
Wu LZhang FCheng CSong S(2024)Supervised Contrast Learning Text Classification Model Based on Data Quality AugmentationACM Transactions on Asian and Low-Resource Language Information Processing10.1145/365330023:5(1-12)Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3653300
Chen HSun YZhang MZhang M(2024)Automatic Noise Generation and Reduction for Text ClassificationIEEE/ACM Transactions on Audio, Speech and Language Processing10.1109/TASLP.2023.332513532(139-150)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1109/TASLP.2023.3325135
Chor WGoh KLim LLum KChiew T(2024)Towards a machine learning-based constructive alignment approach for improving outcomes composure of engineering curriculumEducation and Information Technologies10.1007/s10639-023-12180-y29:7(8925-8959)Online publication date: 1-May-2024
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Tworkowski SStaniszewski KPacek MWu YMichalewski HMiłoś POh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Focused transformerProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667971(42661-42688)Online publication date: 10-Dec-2023
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