Abstract
The paper presents the investigation of collectives of term weighting methods for natural language call routing. The database consists of user utterances recorded in English language from caller interactions with commercial automated agents. Utterances from this database are labelled by experts and divided into 20 classes. Seven different unsupervised and supervised term weighting methods were tested and compared with each other for classification with k-NN. Also a novel feature extraction method based on terms belonging to classes was applied. After that different combinations of term weighting methods were formed as collectives and used for meta-classification with rule induction. The numerical experiments have shown that the combination of two best term weighting methods (Term Relevance Ratio and Confident Weights) increases classification effectiveness in comparison with the best individual term weighting method significantly.
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References
Bengio, Y., Schwenk, H., Senecal, J.-S., Morin, F., and Gauvain, J.-L.: Neural probabilistic language models. In: Innovations in Machine Learning, 137–186 (2006)
Cohen, W.: Fast effective rule induction. In: Proceedings of the Twelfth International Conference on Machine Learning, Lake Tahoe, California (1995)
Collobert, R., Weston, J.: A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th International Conference on Machine Learning, 160–167 (2008)
Debole, F., Sebastiani, F.: Supervised term weighting for automated text categorization. Text mining and its applications, Springer, Berlin Heidelberg, 81–97 (2004)
Gasanova, T., Sergienko, R., Minker, W., Semenkin, E., Zhukov, E.: A semi-supervised approach for natural language call routing. In: Proceedings of the SIGDIAL 2013 Conference, 344–348 (2013)
Gasanova, T., Sergienko, R., Akhmedova, S., Semenkin, E., Minker, W.: Opinion mining and topic categorization with novel term weighting. In: Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Association for Computational Linguistics, Baltimore, Maryland, USA, 84–89 (2014)
Gasanova, T., Sergienko, R., Semenkin, E., Minker, W.: Dimension Reduction with Coevolutionary Genetic Algorithm for Text Classification. In: Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics (ICINCO), Vienna University of Technology, Austria, Vol. 1, 215–222 (2014)
Huang, F., Yates, A.: Distributional representations for handling sparsity in supervised sequencelabeling. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: ACL, Vol. 1, 495–503 (2009)
Ishibuchi, H., Nakashima, T., Murata, T.: Performance evaluation of fuzzy classifier systems for multidimensional pattern classification problems. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 29(5), 601–618 (1999)
Joachims, T.: Learning to classify text using support vector machines: methods, theory and algorithms. Kluwer Academic Publishers, Berlin (2002)
Ko, Y.: A study of term weighting schemes using class information for text classification. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1029–1030 (2012)
Koo, T., Carreras, X., Collins, M.: Simple semisupervised dependency parsing. ACL, 595–603 (2008)
Lan, M., Tan, C.L., Su, J., Lu, Y.: Supervised and traditional term weighting methods for automatic text categorization. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 721–735 (2009)
Miller, S., Guinness, J., Zamanian, A.: Name tagging with word clusters and discriminative training. HLT-NAACL 4, 337–342 (2004)
Mnih, A. Hinton, G.: Three new graphical models for statistical language modelling. In: Proceedings of the 24th International Conference on Machine Learning, 641–648 (2007)
Ratinov, L., Roth, D.: Design challenges and misconceptions in named entity recognition. In: Proceedings of the Thirteenth Conference on Computational Natural Language Learning, ACL, 147–155 (2009)
Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inform. Process. manage. 24(5), 513–523 (1988)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)
Shafait, F., Reif, M., Kofler, C., Breuel, T.M.: Pattern recognition engineering. RapidMiner Community Meeting and Conference, 9 (2010)
Schwenk, H. Gauvain, J.-L.: Connectionist language modeling for large vocabulary continuous speech recognition. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vol. 1 (2002)
Soucy, P., Mineau, G.W.: Beyond TFIDF weighting for text categorization in the Vector space model. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), 1130–1135 (2005)
Wilson, E.B.: Probable inference, the law of succession, and statistical inference. J. Am. Stat. Assoc. 22(158), 209–212 (1927)
Xu, H., Li, C.: A Novel term weighting scheme for automated text Categorization. Intelligent Systems Design and Applications (2007)
Yang, Y., Pedersen, J.O.: A comparative study on feature selection in text categorization. ICML 9, 412–420 (1997)
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Sergienko, R., Gasanova, T., Semenkin, E., Minker, W. (2016). Collectives of Term Weighting Methods for Natural Language Call Routing. In: Filipe, J., Gusikhin, O., Madani, K., Sasiadek, J. (eds) Informatics in Control, Automation and Robotics. Lecture Notes in Electrical Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-319-26453-0_6
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DOI: https://doi.org/10.1007/978-3-319-26453-0_6
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