Abstract
In this paper, we propose a taxonomy for knowledge-oriented question, and study the machine learning based classification for knowledge-oriented Chinese questions. By knowledge-oriented questions, we mean questions carrying information or knowledge about something, which cannot be well described by previous taxonomies. We build the taxonomy after the study of previous work and analysis of 6776 Chinese knowledge-oriented questions collected from different realistic sources. Then we investigate the new task of knowledge-oriented Chinese questions classification based on this taxonomy. In our approach, the popular SVM learning method is employed as classification algorithm. We explore different features and their combinations and different kernel functions for the classification, and use different performance metrics for evaluation. The results demonstrate that the proposed approach is desirable and robust. Thorough error analysis is also conduced.
This work was supported by NSF of China (Grant No. 60473136), the National High Technology Research and Development Major Program of China (863 Program) (Grant No. 2004AA1Z2280), the Doctoral Program Foundation of the China Ministry of Education (Grant No. 20040698028) and the Project of Tackling Key Problems in Science and Technology of Shaanxi province in China (Grant No. 2003K05-G25).
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References
Hermjakob, U.: Parsing and Question Classification for Question Answering. In: Proceedings of the Association for Computational Linguistics 2001 Workshop on Open-Domain Question Answering, pp. 17–22 (2001)
Hovy, E., Hermjakob, U., Ravichandran, D.: A Question/Answer Typology with Surface Text Patterns. In: Proceeding of the Human Language Technology Conference (2002)
Roth, D., Cumby, C., Li, X., Morie, P., Nagarajan, R., Rizzolo, N., Small, K., Yih, W.: Question-Answering via Enhanced Understanding of Questions. In: Proceeding of the 11th Text Retrieval Conference (2002)
Kelly, D., Murdock, V., Yuan, X.J., Croft, W.B., Belkin, N.J.: Features of Documents Relevant to Task- and Fact- Oriented Questions. In: Proceeding of the Eleventh International Conference on Information and Knowledge Management, pp. 645–647 (2002)
Lehnert, W.G.: A Conceptual Theory of Question Answering. In: Natural Language Processing, pp. 651–658 (1986)
Hovy, E., Gerber, L., Hermjakob, U., Junk, M., Lin, C.: Question Answering in Webclopedia. In: Proceedings of the Ninth Text Retrieval Conference, pp. 655–664 (2002)
Li, X., Roth, D.: Learning Question Classifiers. In: Proceedings of the 19th International Conference on Computational Linguistics, pp. 556–562 (2002)
Suzuki, J., Taira, H., Sasaki, Y., Maeda, E.: Question Classification using HDAG Kernel. In: Proceeding of the 41st Annual Meeting of the Association for Computational Linguistics, pp. 61–68 (2003)
http://www.broward.k12.fl.us/learnresource/Info_literacy/Bloom’s_Taxonomy.pdf
Singhal, A., Abney, S., Bacchiani, M., Collins, M., Hindle, D., Pereira, F.: AT&T at TREC-8. In: Proceedings of the 8th Text Retrieval Conference, pp. 317–330 (2000)
Zhang, D., Lee, W.S.: Question Classification using Support Vector Machines. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 26–32 (2003)
Metzler, D., Croft, W.B.: Analysis of Statistical Question Classification for Fact-based Questions. Journal of Information Retrieval, 481–504 (2005)
Zheng, Q., Hu, Y., Zhang, S.: The Research and Implementation of Nature Language Web Answer System. Mini-Micro Systems, 554–560 (2005)
Cortes, C., Vapnik, V.: Support-Vector Networks. Machine Learning, 273–297 (1995)
Joachims, T.: Estimating the Generalization Performance of a SVM Efficiently. In: Proceeding of the International Conference on Machine Learning, pp. 431–438 (2000)
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Hu, Y., Zheng, Q., Bai, H., Sun, X., Dang, H. (2005). Taxonomy Building and Machine Learning Based Automatic Classification for Knowledge-Oriented Chinese Questions. In: Huang, DS., Zhang, XP., Huang, GB. (eds) Advances in Intelligent Computing. ICIC 2005. Lecture Notes in Computer Science, vol 3644. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11538059_51
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DOI: https://doi.org/10.1007/11538059_51
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