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Enhancing Labeled Data Using Unlabeled Data for Topic Tracking

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Human Language Technology Challenges for Computer Science and Linguistics (LTC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8387))

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Abstract

In this paper, we address the problem of dealing with a collection of negative training examples used in the topic tracking task, and propose a method for enhancing positive training examples by using negative training data based on supervised machine learning techniques. We present an algorithm which combines positive example based learning (PEBL) and boosting to learn a set of negative data to train classifiers. The results using Japanese corpus showed that our method attained at 0.161 \(MIN\), and PEBL was 0.332. Similarly, the result using TDT3 showed that the improvement was 0.225 \(MIN\) compared to PEBL.

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Notes

  1. 1.

    http://www.itl.nist.gov/iad/mig/tests/tdt/2000/

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Authors and Affiliations

  1. Interdisciplinary Graduate School of Medicine and Engineering, University of Yamanashi, Kofu, 400-8510, Japan

    Fumiyo Fukumoto, Yoshimi Suzuki & Takeshi Yamamoto

Authors
  1. Fumiyo Fukumoto
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  2. Yoshimi Suzuki
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  3. Takeshi Yamamoto
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Corresponding author

Correspondence to Fumiyo Fukumoto .

Editor information

Editors and Affiliations

  1. Adam Mickiewicz University, Poznań, Poland

    Zygmunt Vetulani

  2. IMMI-CNRS, Orsay, France

    Joseph Mariani

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Fukumoto, F., Suzuki, Y., Yamamoto, T. (2014). Enhancing Labeled Data Using Unlabeled Data for Topic Tracking. In: Vetulani, Z., Mariani, J. (eds) Human Language Technology Challenges for Computer Science and Linguistics. LTC 2011. Lecture Notes in Computer Science(), vol 8387. Springer, Cham. https://doi.org/10.1007/978-3-319-08958-4_17

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  • DOI: https://doi.org/10.1007/978-3-319-08958-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08957-7

  • Online ISBN: 978-3-319-08958-4

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