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AIRS 2015: Information Retrieval Technology pp 203–214Cite as

Smoothing Temporal Difference for Text Categorization

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9460))

Abstract

This paper addresses text categorization problem that training data may be derived from a different time period than test data. We present a method for text categorization that minimizes the impact of temporal effects by using term smoothing and transfer learning techniques. We first used a technique called Temporal-based Term Smoothing (TTS) to replace those time sensitive features with representative terms, then applied boosting based transfer learning algorithm called TrAdaBoost for categorization. The results using a 21-year Japanese Mainichi Newspaper corpus showed that integrating term smoothing and transfer learning improves overall performance, especially it is effective when the creation time period of the test data differs greatly from the training data.

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Notes

  1. 1.

    http://socrates.acadiau.ca/courses/comp/dsilver/NIPS95_LTL/transfer.workshop.1995.html.

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Authors and Affiliations

  1. Graduate Faculty of Interdisciplinary Research, University of Yamanashi, Kofu, Japan

    Fumiyo Fukumoto & Yoshimi Suzuki

Authors
  1. Fumiyo Fukumoto
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  2. Yoshimi Suzuki
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Corresponding author

Correspondence to Fumiyo Fukumoto .

Editor information

Editors and Affiliations

  1. Science and Engineering Faculty, Queensland University of Technology, Brisbane, Australia

    Guido Zuccon

  2. Brisbane, Queensland, Australia

    Shlomo Geva

  3. University of Tsukuba, Ibaraki, Japan

    Hideo Joho

  4. RMIT University, Melbourne, Australia

    Falk Scholer

  5. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Aixin Sun

  6. Tianjin University, China

    Peng Zhang

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© 2015 Springer International Publishing Switzerland

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Fukumoto, F., Suzuki, Y. (2015). Smoothing Temporal Difference for Text Categorization. In: Zuccon, G., Geva, S., Joho, H., Scholer, F., Sun, A., Zhang, P. (eds) Information Retrieval Technology. AIRS 2015. Lecture Notes in Computer Science(), vol 9460. Springer, Cham. https://doi.org/10.1007/978-3-319-28940-3_16

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  • DOI: https://doi.org/10.1007/978-3-319-28940-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28939-7

  • Online ISBN: 978-3-319-28940-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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