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A Multi-classifier Approach to Dialogue Act Classification Using Function Words

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Transactions on Computational Collective Intelligence VII

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 7270))

Abstract

This paper extends a novel technique for the classification of sentences as Dialogue Acts, based on structural information contained in function words. Initial experiments on classifying questions in the presence of a mix of straightforward and “difficult” non-questions yielded promising results, with classification accuracy approaching 90%. However, this initial dataset does not fully represent the various permutations of natural language in which sentences may occur. Also, a higher Classification Accuracy is desirable for real-world applications. Following an analysis of categorisation of sentences, we present a series of experiments that show improved performance over the initial experiment and promising performance for categorising more complex combinations in the future.

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Author information

Authors and Affiliations

  1. Department of Computing and Mathematics, Manchester Metropolitan University, United Kingdom

    James O’Shea, Zuhair Bandar & Keeley Crockett

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  1. James O’Shea
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  2. Zuhair Bandar
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  3. Keeley Crockett
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Editors and Affiliations

  1. InstituteDepartment of Informatics, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

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O’Shea, J., Bandar, Z., Crockett, K. (2012). A Multi-classifier Approach to Dialogue Act Classification Using Function Words. In: Nguyen, N.T. (eds) Transactions on Computational Collective Intelligence VII. Lecture Notes in Computer Science, vol 7270. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32066-8_6

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  • DOI: https://doi.org/10.1007/978-3-642-32066-8_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32065-1

  • Online ISBN: 978-3-642-32066-8

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