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Estimation of Cross-Lingual News Similarities Using Text-Mining Methods

Author

Listed:
  • Zhouhao Wang

    (Izumi lab, Department of System Innovation, Graduate School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan)

  • Enda Liu

    (Izumi lab, Department of System Innovation, Graduate School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan)

  • Hiroki Sakaji

    (Izumi lab, Department of System Innovation, Graduate School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan)

  • Tomoki Ito

    (Izumi lab, Department of System Innovation, Graduate School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan)

  • Kiyoshi Izumi

    (Izumi lab, Department of System Innovation, Graduate School of Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-0033, Japan)

  • Kota Tsubouchi

    (Yahoo! Japan Research, Kioicho 1-3, Chiyoda-ku, Tokyo 102-8282, Japan)

  • Tatsuo Yamashita

    (Yahoo! Japan Research, Kioicho 1-3, Chiyoda-ku, Tokyo 102-8282, Japan)

Abstract

In this research, two estimation algorithms for extracting cross-lingual news pairs based on machine learning from financial news articles have been proposed. Every second, innumerable text data, including all kinds news, reports, messages, reviews, comments, and tweets are generated on the Internet, and these are written not only in English but also in other languages such as Chinese, Japanese, French, etc. By taking advantage of multi-lingual text resources provided by Thomson Reuters News, we developed two estimation algorithms for extracting cross-lingual news pairs from multilingual text resources. In our first method, we propose a novel structure that uses the word information and the machine learning method effectively in this task. Simultaneously, we developed a bidirectional Long Short-Term Memory (LSTM) based method to calculate cross-lingual semantic text similarity for long text and short text, respectively. Thus, when an important news article is published, users can read similar news articles that are written in their native language using our method.

Suggested Citation

  • Zhouhao Wang & Enda Liu & Hiroki Sakaji & Tomoki Ito & Kiyoshi Izumi & Kota Tsubouchi & Tatsuo Yamashita, 2018. "Estimation of Cross-Lingual News Similarities Using Text-Mining Methods," JRFM, MDPI, vol. 11(1), pages 1-13, January.
    • Handle: RePEc:gam:jjrfmx:v:11:y:2018:i:1:p:8-:d:129624
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    Citations

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    Cited by:

    1. Shigeyuki Hamori, 2020. "Empirical Finance," JRFM, MDPI, vol. 13(1), pages 1-3, January.
    2. Kentaka Aruga & Md. Monirul Islam & Yoshihiro Zenno & Arifa Jannat, 2022. "Developing Novel Technique for Investigating Guidelines and Frameworks: A Text Mining Comparison between International and Japanese Green Bonds," JRFM, MDPI, vol. 15(9), pages 1-17, August.

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