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RAST: finding related documents based on triplet similarity

  • ICONIP2009
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Abstract

With the increasing amount of information available in recent years, searching for the desired content is becoming a challenging task. In this work, a tool for searching abstracts submitted to scientific conferences is introduced. It not only searches abstracts by the given keyword(s) but also displays abstracts related to a single or multiple selection. It also displays highly relevant abstracts together with possible keywords to help users refine their search. Analysis of the conditional similarity algorithm proposed here has shown that it does provide better output compared to ordinary cosine similarity, as well as the list of possible keywords reflects results of latent topic analysis. An interface for storing and sorting selected abstracts for future review and/or printing is also provided.

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Author information

Authors and Affiliations

  1. RIKEN Brain Science Institute, Laboratory for Neuroinformatics, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan

    Shiro Usui & Nilton Liuji Kamiji

  2. IVIS Inc, 3-6-6 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan

    Tatsuki Taniguchi

  3. NTT Communication Science Laboratories, 2-4 Hikaridai, Seika-cho, Saraku-gun, Kyoto, 619-0237, Japan

    Naonori Ueda

Authors
  1. Shiro Usui
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  2. Nilton Liuji Kamiji
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  3. Tatsuki Taniguchi
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  4. Naonori Ueda
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Corresponding author

Correspondence to Shiro Usui.

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Usui, S., Kamiji, N.L., Taniguchi, T. et al. RAST: finding related documents based on triplet similarity. Neural Comput & Applic 20, 993–999 (2011). https://doi.org/10.1007/s00521-010-0392-6

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  • DOI: https://doi.org/10.1007/s00521-010-0392-6

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