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Learning Class-Informed Semantic Similarity

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9949))

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Abstract

Exponential kernel, which models semantic similarity by means of a diffusion process on a graph defined by lexicon and co-occurrence information, has been successfully applied to the task of text categorization. However, the diffusion is an unsupervised process, which fails to exploit the class information in a supervised classification scenario. To address the limitation, we present a class-informed exponential kernel to make use of the class knowledge of training documents in addition to the co-occurrence knowledge. The basic idea is to construct an augmented term-document matrix by encoding class information as additional terms and appending to training documents. Diffusion is then performed on the augmented term-document matrix. In this way, the words belonging to the same class are indirectly drawn closer to each other, hence the class-specific word correlations are strengthened. The proposed approach was demonstrated with several variants of the popular 20Newsgroup data set.

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (No. 61562003), the Natural Science Foundation of Jiangxi Province of China (Nos. 20151BAB207029 and 20161BAB202070), the China Scholarship Council (No. 201508360144) and the “Bai Ren Yuan Hang” Project of Jiangxi Province of China in 2015.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Gannan Normal University, Ganzhou, 341000, People’s Republic of China

    Tinghua Wang & Wei Li

Authors
  1. Tinghua Wang
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  2. Wei Li
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Corresponding author

Correspondence to Tinghua Wang .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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Wang, T., Li, W. (2016). Learning Class-Informed Semantic Similarity. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9949. Springer, Cham. https://doi.org/10.1007/978-3-319-46675-0_48

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  • DOI: https://doi.org/10.1007/978-3-319-46675-0_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46674-3

  • Online ISBN: 978-3-319-46675-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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