Abstract
Multi-domain spoken dialog system should be able to detect more than one domain from a user’s utterance. However, it is difficult to train an accurate binary classifier of a domain based on only positive and unlabeled examples. This paper improves hierarchical clustering algorithm to automatically identify reliable negative examples among unlabeled examples. This paper also verifies three linkage criteria that measure the distance between two clusters. In experiments, the proposed method resulted in the highest gain of F 1 score compared to the existing methods.
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References
Chang C, Lin C (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):27:1–27:27
Dempster AP, Laird NM, Rubin DB (1997) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc Ser B Methodol 39(1):1–38
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, 2nd edn. Springer, New York, pp 520–528
Lane I, Kawahara T, Matsui T, Nakamura S (2007) Out-of-domain utterance detection using classification confidences of multiple topics. IEEE Trans Audio Speech Lang Process 15(1):150–161
Li X, Liu B (2003) Learning to classify texts using positive and unlabeled data. In: Proceedings of the 18th international joint conference on artificial intelligence, Acapulco, Mexico, August 2003
Li X, Roth D (2002) Learning question classifiers. In: Proceedings of the 19th international conference on computational linguistics, Taipei, Taiwan, September 2002
Liu B, Lee WS, Yu PS, Li X (2002) Partially supervised classification of text documents. In: Proceedings of the 19th international conference on machine learning, New South Wales, Sydney, July 2002
Liu B, Dai Y, Li X, Lee WS, Yu PS (2003) Building text classifiers using positive and unlabeled examples. In: Proceedings of the 3rd IEEE international conference on data mining, Melbourne, Florida, USA, November 2003
McCallum A, Nigam K (1998) A comparison of event models for Naive Bayes text classification. In: Proceedings of the 15th natural conference on artificial intelligence: workshop on learning from text categorization, Madison, Wisconsin, USA, July 1998
Rocchio J (1971) Relevance feedback in information retrieval. In: The smart retrieval system: experiments in automatic document processing, Englewood Cliffs, New Jersey, USA, 1971
Ryu S, Lee D, Lee I, Han S, Lee GG, Kim M, Kim K (2012) A hierarchical domain model-based multi-domain selection framework for multi-domain dialog systems. In: Proceedings of the 24th international conference on computational linguistics, Mumbai, India, December 2012
Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443–1471
Yu H, Han J, Chang KC (2002) PEBL: positive example based learning for web page classification using SVM. In: Proceedings of the 8th ACM SIGKDD international conference of knowledge discovery and data mining, Edmonton, Alberta, Canada, July 2002
Acknowledgments
This work was supported by ICT R&D program of MSIP/IITP [14-824-09-014, Basic Software Research in Human-level Lifelong Machine Learning (Machine Learning Center)]. This work was supported by National Research Foundation of Korean (NRF) [NRF-2014R1A2A1A01003041, Development of Multi-party Anticipatory Knowledge-Intensive Natural Language Dialog System].
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Ryu, S., Song, J., Koo, S., Kwon, S., Lee, G.G. (2015). Detecting Multiple Domains from User’s Utterance in Spoken Dialog System. In: Lee, G., Kim, H., Jeong, M., Kim, JH. (eds) Natural Language Dialog Systems and Intelligent Assistants. Springer, Cham. https://doi.org/10.1007/978-3-319-19291-8_10
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DOI: https://doi.org/10.1007/978-3-319-19291-8_10
Publisher Name: Springer, Cham
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