Abstract
This study aims to improve the performance of identifying grammatical relations between a noun phrase and a verb phrase in Korean sentences. The key task is to determine the relation between the two constituents in terms of such grammatical relational categories as subject, object, complement, and adverbial. To tackle this problem, we propose to employ the Support Vector Machines (SVM) in determining the grammatical relations. Through an experiment with a tagged corpus for training SVMs, we found the proposed model to be more useful than both the Maximum Entropy model and the backed-off method.
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Berger, A.L., Della Pietra, S.A., Della Pietra, V.J.: A Maximum Entropy Approach to Natural Language Processing. Computational Linguistics 22, 39–71 (1996)
Blaheta, D., Charniak, E.: Assigning function tags to parsed text. In: Proceedings of the 1st Conference of the NAACL, Seattle, WA, pp. 234–240 (2000)
Brants, T., Skut, W., Krenn, B.: Tagging grammatical functions. In: Proceedings of the 2nd Conference on EMNLP, Providence, RI, pp. 64–74 (1997)
Carroll, J., Briscoe, E.: High precision extraction of GRs. In: Proceedings of the 19th International Conference on Computational Linguistics (COLING), Taipei, Taiwan (2002)
Choi, W.S., Cho, J.M., Seo, J.: Analysis System of Speech Acts and Discourse Structures Using Maximum Entropy Model. In: Proceedings Joint 18th International Conference on Computational Linguistics and 37th Annual Meeting of the Association for Computational Linguistics, pp. 230–237 (1999)
Collins, M.: A New Statistical Parser Based on Bigram Lexical Dependencies. In: Proceedings of ACL 1996, Sant Cruz, CA, USA (1996)
Grenfenstette, G.: SQLET: Short query linguistic expansion techniques, palliating one-word queries by providing intermediate structure to text. In: Proc. of the RIAO 1997, pp. 500–509 (1997)
Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Proceedings of European Conference on Machine Learning, pp. 137–142 (1998)
Katz, S.: Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Transactions on Acoustics, Speech, and Signal processing ASSP-35(3) (1987)
Lee, K.J., Kim, J.H., Choi, K.S., Kim, G.C.: Korean syntactic tagset for building a tree annotated corpus. Korean Journal of Cognitive Science 7(4), 7–24 (1996)
Lee, S., Jang, T.Y., Seo, J.: The Grammatical Function Analysis between Adnoun Clause and Noun Phrase in Korean. In: Proceedings of Sixth Natural Language Processing Pacific Rim Symposium, pp. 709–713 (2001)
Lee, S., Seo, J., Jang, T.Y.: Analysis of the grammatical functions between adnoun and NPs in Korean using Support Vector Machines. Natural Language Engineering 9(3), 269–280 (2003)
Palmer, M., Passonneau, R., Weir, C., Finin, T.: The KERNEL text understanding system. Artificial Intelligence 63, 17–68 (1993)
Ratnaparkhi, A.: Learning to Parse Natural Language with Maximum Entropy Models. Machine Learning 34, 151–176 (1999)
van Rijsbergen, C.J.: Information Retrieval. Buttersworth, London (1979)
Ristad, E.: Maximum Entropy Modeling Toolkit. Technical Report, Department of Computer Science, Princeton University (1996)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Witten, I.T., Bell, T.C.: The zero-frequency problem: estimating the probabilities of novel events in adaptive text compression. IEEE Transactions on Information Theory 37(4), 1085–1094 (1991)
Yeh, A.: Using existing systems to supplement small amounts of annotated grammatical relations training data. In: Proceedings of the ACL 2000, Hong Kong, pp. 126–132 (2000)
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Lee, S., Seo, J. (2004). Grammatical Relations Identification of Korean Parsed Texts Using Support Vector Machines. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2004. Lecture Notes in Computer Science(), vol 3206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30120-2_16
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DOI: https://doi.org/10.1007/978-3-540-30120-2_16
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