Abstract
We present a knowledge-rich approach to Japanese coreference resolution. In Japanese, proper noun coreference and common noun coreference occupy a central position in coreference relations. To improve coreference resolution for such language, wide-coverage knowledge of synonyms is required. We first acquire knowledge of synonyms from large raw corpus and dictionary definition sentences, and resolve coreference relations based on the knowledge. Furthermore, to boost the performance of coreference resolution, we integrate bridging reference resolution system into coreference resolver.
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References
Yang, X., et al.: Improving noun phrase coreference resolution by matching strings. In: Su, K.-Y., et al. (eds.) IJCNLP 2004. LNCS (LNAI), vol. 3248, pp. 326–333. Springer, Heidelberg (2005)
Soon, W.M., Ng, H.T., Lim, D.C.Y.: A machine learning approach to coreference resolution of noun phrases. Computational Linguistics 27(4), 521–544 (2001)
Ng, V.: Machine learning for coreference resolution: From local classification to global ranking. In: Proceedings of the 43rd Annual Meeting of the Asssociation for Computational Linguistics, pp. 157–164 (2005)
Luo, X., et al.: A mention-synchronous coreference resolution algorithm based on the bell tree. In: Proceedings of the 42nd Meeting of the Association for Computational Linguistics, pp. 135–142 (2004)
Tajika, J.-i.: Reikai Syogaku Kokugojiten. Sanseido (1997)
Nishio, M., Iwabuti, E., Mizutani, S.: Iwanami Kokugo Jiten. Iwanami Shoten (2000)
Kurohashi, S., Nagao, M.: A syntactic analysis method of long Japanese sentences based on the detection of conjunctive structures. Computational Linguistics 20(4), 507–534 (1994)
Isozaki, H., Kazawa, H.: Efficient support vector classifiers for named entity recognition. In: Proceedings of the 19th International Conference on Computational Linguistics, pp. 390–396 (2002)
Sasano, R., Kawahara, D., Kurohashi, S.: Automatic construction of nominal case frames and its applicatoin to indirect anaphora resolution. In: Proceedings of the 20th International Conference on Computational Linguistics, pp. 1201–1207 (2004)
Kawahara, D., Kurohashi, S., Hasida, K.: Construction of a Japanese Relevance-tagged Corpus. In: Proceedings of the 3rd International Conference on Language Resources and Evaluation, pp. 2008–2013 (2002)
Murata, M., Nagao, M.: An estimate of referent of noun phrases in Japanese sentences. In: Proceedings of 36th Annual Meeting of the Association for Computational Linguistics and the 17th International Conference on Computational Linguistics, pp. 912–916 (1998)
Iida, R., et al.: Noun phrase coreference resolution in japanese most likely candidate antecedents (in japanese). Journal of Information Processing Society of Japan 46(3), 831–844 (2005)
Ng, V., Cardie, C.: Improving machine learning approaches to coreference resolution. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, pp. 104–111 (2002)
Bean, D., Riloff, E.: Unsupervised learning of contextual role knowledge for coreference resolution. In: Proceedings of the Human Language Technology Conference / North American Chapter of the Assosication for Computational Linguistics Annual Meeting, pp. 297–304 (2004)
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Sasano, R., Kawahara, D., Kurohashi, S. (2007). Improving Coreference Resolution Using Bridging Reference Resolution and Automatically Acquired Synonyms. In: Branco, A. (eds) Anaphora: Analysis, Algorithms and Applications. DAARC 2007. Lecture Notes in Computer Science(), vol 4410. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71412-5_9
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DOI: https://doi.org/10.1007/978-3-540-71412-5_9
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