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Weak Supervision for Semi-supervised Topic Modeling via Word Embeddings

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Language, Data, and Knowledge (LDK 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10318))

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Abstract

Semi-supervised algorithms have been shown to improve the results of topic modeling when applied to unstructured text corpora. However, sufficient supervision is not always available. This paper proposes a new process, Weak+, suitable for use in semi-supervised topic modeling via matrix factorization, when limited supervision is available. This process uses word embeddings to provide additional weakly-labeled data, which can result in improved topic modeling performance.

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Acknowledgement

This research was partly supported by Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.

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Authors and Affiliations

  1. School of Computer Science, University College Dublin, Dublin, Ireland

    Gerald Conheady & Derek Greene

Authors
  1. Gerald Conheady
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  2. Derek Greene
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Corresponding author

Correspondence to Gerald Conheady .

Editor information

Editors and Affiliations

  1. Universidad Politécnica de Madrid, Madrid, Spain

    Jorge Gracia

  2. Nanyang Technological University, Singapore, Singapore

    Francis Bond

  3. Insight Centre for Data Analytics, National University of Ireland, Galway, Galway, Ireland

    John P. McCrae

  4. Insight Centre for Data Analytics, National University of Ireland, Galway, Ireland

    Paul Buitelaar

  5. Goethe-University Frankfurt, Frankfurt, Germany

    Christian Chiarcos

  6. University of Leipzig, Leipzig, Germany

    Sebastian Hellmann

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Conheady, G., Greene, D. (2017). Weak Supervision for Semi-supervised Topic Modeling via Word Embeddings. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds) Language, Data, and Knowledge. LDK 2017. Lecture Notes in Computer Science(), vol 10318. Springer, Cham. https://doi.org/10.1007/978-3-319-59888-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-59888-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59887-1

  • Online ISBN: 978-3-319-59888-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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