Abstract
A speech act is a linguistic action intended by a speaker. It is important to analyze the speech act for the dialogue understanding system because the speech act of an utterance is closely tied with the user’s intention in the utterance. This paper proposes to use a speech acts hierarchy and a discourse stack for improving the accuracy of classifiers in speech acts analysis. We first adopt a hierarchical statistical technique called shrinkage to solve the data sparseness problem. In addition, we use a discourse stack in order to easily apply discourse structure information to the speech acts analysis. From the results of experiments, we observed that the proposed model made a significant improvement for Korean speech acts analysis. Moreover, we found that it can be more useful when training data is insufficient.
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References
Choi, W., Cho, J., Seo, J.: Analysis System of speech acts and Discourse Structures Using Maximum Entropy Model. In: Proceedings of COLING-ACL 1999, pp. 230–237 (1999)
Grosz, B.: Discourse and Dialogue. Survey of the State of the Art in Human Language Technology, Center for Spoken Language Understanding, pp. 227–254 (1995)
James, W., Stein, C.: Estimation with Quadratic Loss. In: Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 361–379. University of California Press
Joachims, T.: Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In: European conference on machine learning (ECML), pp. 137–142 (1998)
Kim, K., Kim, H., Seo, J.: A Neural Network Model with Feature Selection for Korean Speech Act Classification. International Journal of Neural System 14(6), 407–414 (2004)
Ko, Y., Park, J., Seo, J.: Improving Text Categorization Using the Importance of Sentences. Information Processing & Management 40(1), 65–79 (2004)
Lee, J., Kim, G., Seo, J.: A Dialogue Analysis Model with Statistical Speech Act Processing for Dialogue Machine Translation. In: Proceedings of ACL Workshop on Spoken Language Translation, pp. 10–15 (1997)
Lee, S., Seo, J.: A Korean Speech Act Analysis System Using Hidden Markov Model with Decision Trees. International Journal of Computer Processing of Oriental Languages 15(3), 231–243 (2002)
MacCallum, A., Rosenfeld, R., Mitchell, T., Ng, A.Y.: Improving Text Classification by Shrinkge in a Hierarchy of Classes. In: Proceedings of the International Conference on Machine Learning (1998)
Samuel, K., Caberry, S., Vijay-Shanker, K.: Automatically Selecting Useful Phrases for Dialogue Act Tagging. In: Proceedings of the Fourth Conference of the Pacific Association for Computational Linguistics (1999)
Tanaka, H., Yokoo, A.: An Efficient Statistical Speech Act Type Tagging System for Speech Translation Systems. In: Proceedings of COLING-ACL 1999, pp. 381–388 (1999)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
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© 2005 Springer-Verlag Berlin Heidelberg
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Kim, K., Ko, Y., Seo, J. (2005). Improving Korean Speech Acts Analysis by Using Shrinkage and Discourse Stack. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_64
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DOI: https://doi.org/10.1007/11562214_64
Publisher Name: Springer, Berlin, Heidelberg
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