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Review of Korean Speech Act Classification: Machine Learning Methods 원문보기

Journal of computing science and engineering, v.5 no.4, 2011년, pp.288 - 293  

Kim, Hark-Soo (Department of Computer and Communications Engineering, Kangwon National University) ,  Seon, Choong-Nyoung (Department of Computer Science and Engineering, Sogang University) ,  Seo, Jung-Yun (Department of Computer Science and Engineering)

Abstract ▼ AI-Helper 아이콘AI-Helper

To resolve ambiguities in speech act classification, various machine learning models have been proposed over the past 10 years. In this paper, we review these machine learning models and present the results of experimental comparison of three representative models, namely the decision tree, the supp...

주제어

#Korean speech act classification   #Machine learning method  

AI 본문요약
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제안 방법

  • First we reviewed a representative statistical model. Based on the statistical model, we reviewed three groups of machine learning models: a rule-based group, a margin-based group, and a statistics-based group. In the experiments with a goal-oriented dialogue corpus in a schedule management domain, we selected a single representative per group among previous models: C4.
  • In recent years, there has been increased interest in using statistical and machine learning approaches. In this paper, we review a representative probabilistic model for speech act classification and then compare various machine learning models to it.
  • The first experiment performed evaluated the memory requirements and processing speeds of the various models. Table 5 shows the results of the first experiment.
  • A support vector machine (SVM) and a multilayer perceptron (MLP; a feed forward artificial neural network model) have shown good performance in speech act classification [20, 21]. The goal of an SVM is to find the particular hyperplane that maximizes the margin of separation between a cluster of positive examples and a cluster of negative examples. An SVM transforms the given non-linear problems into linear problems by projecting input features into higher dimensions and then quickly solving the given problems high performance.

대상 데이터

  • A total of 3,000 sentential features were selected based on the χ2 statistic in Equation (8) for each SVM and MEM. Because the feature selection did not improve DT performance, it used all of the sentential features (10,082 features). The toolkits used for implementations included C4.
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참고문헌 (27)

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    상세보기 crossref

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    상세보기 crossref

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  6. N. Reithinger and M. Klesen, "Dialogue act classification using language models," Proceedings of EuroSpeech, Rhodos, Greece, 1997, pp. 2235-2238. 

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  8. A. Stolcke, N. Coccaro, R. Bates, P. Taylor, C. Van Ess-Dykema, K. Ries, E. Shriberg, D. Jurafsky, R. Martin, and M. Meteer, "Dialogue act modeling for automatic tagging and recognition of conversational speech," Computational Linguistics, vol. 26, no. 3, pp. 339-373, 2000. 

    상세보기 crossref

  9. C. T. Langley, "Analysis for speech translation using grammarbased parsing and automatic classification," Proceedings of Student Research Workshop at the 40th Annual Meeting of the Association of Computational Linguistics, Philadelphia, PA, 2002, July. 

  10. H. Kim and J. Seo, "An efficient trigram model for speech act analysis in small training corpus," Journal of Cognitive Science, vol. 4, no. 1, pp. 107-120, 2003. 

  11. N. Webb, M. Hepple, and Y. Wilks, "Dialogue act classification based on intra-utterance features," Proceedings of the AAAI Workshop on Spoken Language Understanding, Pittsburgh, PA, 2005, July 9-10. 

  12. W. S. Choi, H. Kim, and J. Seo, "An integrated dialogue analysis model for determining speech acts and discourse structures," IEICE Transactions on Information and Systems, vol. E88-D, no. 1, pp. 150-157, 2005. 

  13. H. Lee, H. Kim, and J. Seo, "Domain action classification using a maximum entropy model in a schedule management domain," AI Communications, vol. 21, no. 4, pp. 221-229, 2008. 

    상세보기

  14. S. Kang, H. Kim, and J. Seo, "A reliable multidomain model for speech act classification," Pattern Recognition Letters, vol. 31, no. 1, pp. 71-74, 2010. 

    상세보기 crossref

  15. M. J. Kim, J. H. Park, S. B. Kim, H. C. Rim, and D. G. Lee, "A comparative study on optimal feature identification and combination for korean dialogue act classification," Journal of Korean Institute of Information Scientists and Engineers: Software and Applications, vol. 35, no. 11, pp. 681-691, Nov 2008. 

  16. K. Kim, H. Kim, and J. Seo, "A neural network model with feature selection for Korean speech act classification," International Journal of Neural Systems, vol. 14, no. 6, pp. 407-414, 2004. 

    상세보기 crossref

  17. Y. Yang and J. O. Pedersen, "A comparative study on feature selection in text categorization," Proceedings of the 14th International Conference on Machine Learning, Nashville, TN, 1997, July, pp. 412-420. 

  18. E. Charniak, Statistical Language Learning, Cambridge, MA: MIT Press, 1993. 

  19. S. Lee and J. Seo, "Korean speech act analysis system using hidden markov model with decision trees," International Journal of Computer Processing of Oriental Languages, vol. 15, no. 3, pp. 231-243, 2002. 

    상세보기 crossref

  20. D. Surendran and G. A. Levow, "Dialogue act tagging with support vector machines and hidden markov models," Proceedings of Interspeech/ICSLP, Pittsburgh, PA, 2006, Sep. 

  21. H. Lee, H. Kim, and J. Seo, "Efficient domain action classification using neural networks," Neural Information Processing. Lecture Notes in Computer Science Vol. 4223, I. King, J. Wang, L. W. Chan, and D. Wang, Eds., Heidelberg, Germany: Springer Berlin, 2006, pp. 150-158. 

  22. D. Kim, H. Kim, and J. Seo, "A statistical prediction model of speakers' intentions in a goal-oriented dialogue," Journal of Korean Institute of Information Scientists and Engineers: Software and Applications, vol. 35, no. 9, pp. 554-561, Sep 2008. 

  23. J. D. Lafferty, A. McCallum, and F. C. N. Pereira, "Conditional random fields: probabilistic models for segmenting and labeling sequence data," Proceedings of the Eighteenth International Conference on Machine Learning, Williamstown, MA, 2001, June 28-July 1, pp. 282-289. 

  24. J. C. Reynar and A. Ratnaparkhi, "A maximum entropy approach to identifying sentence boundaries," Proceedings of the 5th Conference on Applied Natural Language Processing, Washington, DC, 1997, March 31-April 3, pp. 16-19. 

  25. J. R. Quinlan, C4.5: Programs for Machine Learning, San Mateo, CA: Morgan Kaufmann Publishers, 1993. 

  26. T. Joachims, "SVMLight: Support Vector Machine Version: 6.02," http://svmlight.joachims.org/. 

  27. E. S. Ristad, Maximum entropy modeling toolkit, Technical Report, Department of Computer Science, Princeton University, 1996. 

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