Abstract
This study aims to identify dependency structure in Korean sentences with the cascaded chunking strategy. In the first stages of the cascade, we find chunks of NP and guess grammatical relations (GRs) using Support Vector Machine (SVM) classifiers for every possible modifier-head pairs of chunks in terms of GR categories as subject, object, complement, adverbial, and etc. In the next stage, we filter out incorrect modifier-head relations in each cascade for its corresponding GR using the SVM classifiers and the characteristics of the Korean language such as distance, no-crossing and case property. Through an experiment with a tree and GR tagged corpus for training the proposed parser, we achieved an overall accuracy of 85.7% on average.
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References
Brants, T., Skut, W., Krenn, B.: Tagging grammatical functions. In: Proc. of the 2nd Conference on EMNLP, pp. 64–74 (1997)
Argamon, S., Dagan, I., Krymolowski, Y.: A memory-based approach to learning shallow natural language patterns. In: Proc. of the 36th Annual Meeting of the ACL, pp. 67–73 (1998)
Buchholz, S., Veenstra, J., Daelemans, W.: Cascaded GR assignment. In: Proc. of the Joint Conference on EMNLP and Very Large Corpora, pp. 239–246 (1999)
Blaheta, D., Charniak, E.: Assigning function tags to parsed text. In: Proc. of the 1st Conference of the NAACL, pp. 234–240 (2000)
Carroll, J., Briscoe, E.: High precision extraction of grammatical relations. In: Proc. of the 19th International Conference on Computational Linguistics, pp. 134–140 (2002)
Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: Proc. of European Conference on Machine Learning, pp. 137–142 (1998)
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)
Lee, K.J., Kim, J.H., Kim, G.C.: An Efficient Parsing of Korean Sentence Using Restricted Phrase Structure Grammar. Computer Processing of Oriental Languages 12(1), 49–62 (1997)
Viterbi, A.J.: Error bounds for convolution codes and an asymptotically optimal decoding algorithm. IEEE trans. on Information Theory 12, 260–269 (1967)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
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)
van Rijsbergen, C.J.: Information Retrieval. Buttersworth, London (1979)
Lee, S.: A statistical model for identifying grammatical relations in Korean sentences. IEICE transactions on Information and Systems E87-D(12), 2863–2871 (2004)
Lee, S., Seo, J.: Grammatical relations identification of Korean parsed texts using support vector machines. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2004. LNCS (LNAI), vol. 3206, pp. 121–128. Springer, Heidelberg (2004)
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Lee, S. (2006). Cascaded Grammatical Relation-Driven Parsing Using Support Vector Machines. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2006. Lecture Notes in Computer Science(), vol 4188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11846406_32
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DOI: https://doi.org/10.1007/11846406_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-39090-9
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