Abstract
Question target identification and classification is a fundamental and essential research for finding suitable target answer type in a question answering system, aiming for improving question answering performance by filtering out irrelevant candidate answers. This paper presents a new automated approach for question target classification based on WordNet expansion. Our approach identifies question target words using dependency relations and answer type rules through the investigation of sample questions. Leveraging semantic relations, e.g., hyponymy, we expanse the question target words as features and apply a widely used classifier LibSVM to achieve question target classification. Our experiment datasets are the standard UIUC 5500 annotated questions and TREC 10 question dataset. The performance presents that our approach can achieve an accuracy of 87.9 % with fine gained classification on UIUC dataset and 86.8 % on TREC 10 dataset, demonstrating its effectiveness.
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Le-Hong, P., Phan, X.-H., Nguyen, T.-D.: Using dependency analysis to improve question classification. In: Nguyen, V.-H., Le, A.-C., Huynh, V.N. (eds.). AISC, vol. 326, pp. 673–686Springer, Heidelberg (2015)
Ray, S.K., Shailendra, S., Bhagwati, P.J.: A semantic approach for question classification using WordNet and Wikipedia. Pattern Recogn. Lett. 31(13), 1935–1943 (2010)
Moldovan, D., Paşca, M., Harabagiu, S., Surdeanu, M.: Performance issues and error analysis in an open-domain question answering system. ACM Trans. Inf. Syst. (TOIS) 21(2), 133–154 (2003)
Li, X., Roth, D.: Learning question classifiers. In: Proceedings of COLING (2002)
Voorhees, E.M.: Overview of the TREC 2003 Question Answering Track. TREC (2003)
Miller, G., Christiane, F.: WorldNet: An electronic lexical database. MIT Press, Cambridge (1998)
Huang, Z.H., Thint, M., Qin, Z.C: Question classification using head words and their hypernyms. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP) (2008)
Bakhtyar, M., Kawtrakul, A., Baber, J., Doudpota, S.M.: Creating multi-level class hierarchy for question classification with NP analysis and WordNet. J. Digit. Inf. Manage. 10(6), 379 (2012)
Martin, A.I., Franz, M., Roukos, S.: IBM’s statistical question answering system-TREC-10. In: Proceedings of the Tenth Text REtrieval Conference (TREC) (2001)
Moschitti, A., Quarteroni, S.: Kernels on linguistic structures for answer extraction. In: Proceedings of ACL (2008)
Quarteroni, S., Moschitti, R., Man, S., Basili, R.: Advanced structural representations for question classification and answer re-ranking. LNCS, pp. 234–245 (2007)
Ray, S.K., Singh, S., Joshi, B.P.: A semantic approach for question classification using WordNet and Wikipedia. Pattern Recogn. Lett. 31(13), 1935–1943 (2010)
Wu, Y.Z., Zhang, R.Q., Hu, X.H., Kashioka, H.: Learning unsupervised SVM classifier for answer selection in web question answering. In: Proceedings of EMNLP-CoNLL, pp. 33–41 (2007)
Sasaki, Y.: Question answering as question-biased term extraction: a new approach toward multilingual. In: Proceedings of ACL, pp. 215–222 (2005)
Ogawa, K., Takeuchi, I.: Safe screening of non-support vectors in pathwise SVM computation. In: Proceedings of International Conference on Machine Learning (ICML) (2013)
Yen, S.J., Wu, Y.C., Yang, J.C., Lee, Y.S., Lee, C.J., Liu, J.J.: A support vector machine-based context-ranking model for question answering. Inf. Sci. 224(2), 77–87 (2013)
Hardy, H., Cheah, Y.N.: Question classification using extreme learning machine on semantic features. J. ICT Res. Appl. 7(1), 36–58 (2013)
Jeong, Y., Myaeng, S.-H.: Using wordnet hypernyms and dependency features for phrasal-level event recognition and type classification. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 267–278. Springer, Heidelberg (2013)
Gao, J.B., Zhang, B.W., Chen, X.H.: A WordNet-based semantic similarity measurement combining edge-counting and information content theory. Eng. Appl. Artif. Intell. 80–88 (2015)
Barbu, E.: Property type distribution in WordNet, corpora and Wikipedia. Expert Syst. Appl. 42(7), 3501–3507 (2015)
Berwick, R.C.: Principles of principle-based parsing, pp. 1–37. Principle-Based Parsing. Springer, Netherlands (1992)
Hao, T.Y., Xu, F.F., Lei, J.S., Liu, W.Y., Li, Q.: Toward automatic answers in user-interactive question answering systems. Int. J. Softw. Sci. Comput. Intell. 3(4), 52–66 (2011)
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This work was supported by National Natural Science Foundation of China (grant No. 61403088 and No.61305094).
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Hao, T., Xie, W., Xu, F. (2015). A WordNet Expansion-Based Approach for Question Targets Identification and Classification. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_27
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DOI: https://doi.org/10.1007/978-3-319-25816-4_27
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