Abstract
The paper describes a research about the possibility of integrating different types of word and semantic features for automatically identifying themes of real-life telephone conversations in a customer care service (CCS). Features are all the words of the application vocabulary, the probabilities obtained with latent Dirichlet allocation (LDA) of selected discriminative words and semantic features obtained with a limited human supervision of words and patterns expressing entities and relations of the application ontology. A deep neural network (DNN) is proposed for integrating these features. Experimental results on manual and automatic conversation transcriptions are presented showing the effective contribution of the integration. The results show how to automatically select a large subset of the test corpus with high precision and recall, making it possible to automatically obtain theme mention proportions in different time periods.
Mohamed Bouallegue thanks the ANR agency for funding through the CHIST-ERA ERA-Net JOKER project.
Carole Lailler thanks European Commission for funding through the EUMSSI Project, number 611057, call FP7-ICT-2013-10.
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Estève, Y. et al. (2015). Integration of Word and Semantic Features for Theme Identification in Telephone Conversations. 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_21
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DOI: https://doi.org/10.1007/978-3-319-19291-8_21
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