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Transductive Learning with String Kernels for Cross-Domain Text Classification

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11303))

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Abstract

For many text classification tasks, there is a major problem posed by the lack of labeled data in a target domain. Although classifiers for a target domain can be trained on labeled text data from a related source domain, the accuracy of such classifiers is usually lower in the cross-domain setting. Recently, string kernels have obtained state-of-the-art results in various text classification tasks such as native language identification or automatic essay scoring. Moreover, classifiers based on string kernels have been found to be robust to the distribution gap between different domains. In this paper, we formally describe an algorithm composed of two simple yet effective transductive learning approaches to further improve the results of string kernels in cross-domain settings. By adapting string kernels to the test set without using the ground-truth test labels, we report significantly better accuracy rates in cross-domain English polarity classification.

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Authors and Affiliations

  1. University of Bucharest, 14 Academiei, Bucharest, Romania

    Radu Tudor Ionescu & Andrei Madalin Butnaru

Authors
  1. Radu Tudor Ionescu
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  2. Andrei Madalin Butnaru
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Correspondence to Radu Tudor Ionescu .

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Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Ionescu, R.T., Butnaru, A.M. (2018). Transductive Learning with String Kernels for Cross-Domain Text Classification. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11303. Springer, Cham. https://doi.org/10.1007/978-3-030-04182-3_42

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  • DOI: https://doi.org/10.1007/978-3-030-04182-3_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04181-6

  • Online ISBN: 978-3-030-04182-3

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