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Mining Automatic Speech Transcripts for the Retrieval of Problematic Calls

  • Conference paper
Computational Linguistics and Intelligent Text Processing (CICLing 2013)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7817))

Abstract

In order to assure and to improve the quality of service, call center operators need to automatically identify the problematic calls in the mass of information flowing through the call center. Our method to select and rank those critical conversations uses linguistic text mining to detect sentiment markers on French automatic speech transcripts. The markers’ weight and orientation are used to calculate the semantic orientation of the speech turns. The course of a conversation can then be graphically represented with positive and negative curves. We have established and evaluated on a manually annotated corpus three heuristics for the automatic selection of problematic conversations. Two proved to be very useful and complementary for the retrieval of conversations having segments with anger and tension. Their precision is high enough for use in real world systems and the ranking evaluated by mean precision follows the usual relevance behavior of a search engine.

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Authors and Affiliations

  1. Sinequa, 12 rue d’Athènes, F-75009, Paris, France

    Frederik Cailliau & Ariane Cavet

Authors
  1. Frederik Cailliau
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  2. Ariane Cavet
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Editor information

Editors and Affiliations

  1. Center for Computing Research, National Polytechnic Institute, Mexico D.F., Mexico

    Alexander Gelbukh

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Cailliau, F., Cavet, A. (2013). Mining Automatic Speech Transcripts for the Retrieval of Problematic Calls. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, vol 7817. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37256-8_8

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  • DOI: https://doi.org/10.1007/978-3-642-37256-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37255-1

  • Online ISBN: 978-3-642-37256-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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