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Adapt a Text-Oriented Chunker for Oral Data: How Much Manual Effort Is Necessary?

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Intelligent Data Engineering and Automated Learning – IDEAL 2013 (IDEAL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8206))

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Abstract

In this paper, we try three distinct approaches to chunk transcribed oral data with labeling tools learnt from a corpus of written texts. The purpose is to reach the best possible results with the least possible manual correction or re-learning effort.

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Author information

Authors and Affiliations

  1. Lattice, University Paris 3 - Sorbonne Nouvelle, France

    Isabelle Tellier, Yoann Dupont & Ilaine Wang

  2. LLL, University of Orléans, France

    Iris Eshkol

Authors
  1. Isabelle Tellier
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  2. Yoann Dupont
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  3. Iris Eshkol
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  4. Ilaine Wang
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

  2. University of Science and Technology of China, Hefei, China

    Ke Tang

  3. Nanjing University, Nanjing, China

    Yang Gao

  4. Ostfalia University of Applied Sciences, 38302, Wolfenbüttel, Germany

    Frank Klawonn

  5. Kyungpook National University, 702-701, Buk-Gu, Daegu, Korea

    Minho Lee

  6. Nature Inspired Computational and Applications Laboratory, School of Computer Science and Technology,, University of Science and Technology of China, 230027, Hefei, China

    Thomas Weise

  7. University of Science and Technology of China, 230017, Hefei, China

    Bin Li

  8. CERCIA, School of Computer Science, University of Birmingham, B15 2TT, Edgbaston, Birmingham, UK

    Xin Yao

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Tellier, I., Dupont, Y., Eshkol, I., Wang, I. (2013). Adapt a Text-Oriented Chunker for Oral Data: How Much Manual Effort Is Necessary?. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_28

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  • DOI: https://doi.org/10.1007/978-3-642-41278-3_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41277-6

  • Online ISBN: 978-3-642-41278-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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