Abstract
Question classification has been recognized as a very important step for many natural language applications (i.e question answering). Subtree mining has been indicated that [10] it is helpful for question classification problem. The authors empirically showed that subtree features obtained by subtree mining, were able to improve the performance of Question Classification for boosting and maximum entropy models. In this paper, our first goal is to investigate that whether or not subtree mining features are useful for structured support vector machines. Secondly, to make the proposed models more robust, we incorporate subtree features with word-cluster models gained from a large collection of text documents. Experimental results show that the uses of word-cluster models with subtree mining can significantly improve the performance of the proposed question classification models.
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References
Berger, A., Pietra, S.D., Pietra, V.D.: A maximum entropy approach to natural language processing. Computational Linguistics 22(1) (1996)
Brown, P.F., Della Pietra, V.J., de Souza, P.V., Lai, J.C., Mercer, R.L.: Class-Based n-gram Models of Natural Language. Computational Linguistics 18(4), 467–479 (1992)
Charniak, E.: A Maximum-Entropy Inspired Parser. In: Proc. ACL (2001)
Charniak, E., Blaheta, D., Ge, N., Hall, K., Hale, J., Johnson, M.: BLLIP 1987-1989 WSJ Corpus Release 1. Linguistic Data Consortium (2000)
Carlson, A., Cumby, C., Roth, D.: The SNoW learning architecture, Technical report UIUC-DCS-R-99-2101, UIUC Computer Science Department (1999)
Kadri, H., Wayne, W.: Question classification with Support vector machines and error correcting codes. In: Proceedings of NAACL-HLT 2003, pp. 28–30 (2003)
Kudo, T., Maeda, E., Matsumoto, Y.: An Application of Boosting to Graph Classification. In: Proceedings NIPS (2004)
Li, X., Roth, D.: Learning question classifiers. In: Proceedings of the 19th International Conference on Computational Linguistics, pp. 556–562 (2002)
Liang, P., Collins, M.: Semi-supervised learning for natural language. Master thesis, MIT (2005)
Nguyen, M.L., Shimazu, A., Nguyen, T.T.: Subtree mining for question classification problem. In: Proceedings IJCAI 2007, pp. 1695–1700 (2007)
Morishita, S.: Computing optimal hypotheses efficiently for boosting. In: Arikawa, S., Shinohara, A. (eds.) Progress in Discovery Science. LNCS (LNAI), vol. 2281, pp. 471–481. Springer, Heidelberg (2002)
Tsochantaridis, I., Hofmann, T., Joachims, T., Altun, Y.: Support Vector Machine Learning for Interdependent and Structured Output Spaces. In: Proceedings ICML 2004 (2004)
Zhang, D., Lee, W.S.: Question classification using Support vector machine. In: Proceedings of ACM SIGIR-2033, pp. 26–33 (2033)
Zaki, M.J.: Efficiently Mining Frequent Trees in a Forest. In: Proceedings 8th ACM SIGKDD 2002 (2002)
Schapire: A brief introduction to boosting. In: Proceedings of IJCAI 1999 (1999)
Radev, D.R., Fan, W., Qi, H., Wu, H., Grewal, A.: Probabilistic Question Answering from the Web. In: Proceedings of WWW (2002)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, N.Y (1995)
Voorhees, E.: Overview of the TREC 2001 Question Answering Track. In: Proceedings of TREC 2010, pp. 157–165. NIST, Gaithersburg (2001)
Ray, S.K., Singh, S., Joshi, B.P.: A semantic approach for question classification using WordNet and Wikipedia. Pattern Recognition Letters 31(13), 1935–1943 (2010)
Huang, Z., Thint, M., Kin, Z.: Question classification using head words and their hypernyms. In: Proceedings EMNLP 2008, pp. 927–936 (2008)
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Nguyen, L.M., Shimazu, A. (2011). Improving Subtree-Based Question Classification Classifiers with Word-Cluster Models. In: Muñoz, R., Montoyo, A., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2011. Lecture Notes in Computer Science, vol 6716. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22327-3_8
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DOI: https://doi.org/10.1007/978-3-642-22327-3_8
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