Feature extraction for learning to classify questions

Authors:
Zhalaing Cheung;Khanh Linh Phan;Ashesh Mahidadia;Achim Hoffmann
Affiliations:
School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW, Australia
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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Abstract

In this paper, we present a new approach to learning the classification of questions Question classification received interest recently in the context of question answering systems for which categorizing a given question would be beneficial to allow improved processing of the document to identify an answer Our approach relies on relative simple preprocessing of the question and uses standard decision tree learning We also compared our results from decision tree learning with those obtained using Naïve Bayes Both results compare favorably to several very recent studies using more sophisticated preprocessing and/or more sophisticated learning techniques Furthermore, the fact that decision tree learning proved more successful than Naïve Bayes is significant in itself as decision tree learning is usually believed to be less suitable for NLP tasks.