Researchers

 

Researchers

SEKI, Kazuhiro

SEKI, KazuhiroAs of Apr 2022

Professor, Konan University

Text analytics, Data science, Machine learning, and their application to real-world problems to develop intelligent information processing/management systems.

Educational background

  • Ph.D. in Information Science (Indiana University, Bloomington)

Career

  • Professor, Konan University, 2021-Present
  • Associate Professor, Konan University, 2014-2021

Books and Chapters

  • “Measuring Social Change Using Text Data: A Simple Distributional Approach.”
  • Reconstruction of the Public Sphere in the Socially Mediated Age. Springer, pp. 139-164, November 2017. (with Takashi Kamihigashi and Masahiko Shibamoto.)
  • “Statistical Anaphora Resolution for Japanese Zero Pronouns. ”
  • Readings in Japanese Natural Language Processing. CSLI Publications, pp. 108-128, June 2016. (with Atsushi Fujii and Tetsuya Ishikawa.)

Journal Articles and Papers

  • “News-based Business Sentiment and its Properties as an Economic Index. ”
  • Information Processing & Management (IP&M), Vol. 59, No. 2, 102795, March 2022.
  • (with Yusuke Ikuta and Yoichi Matsubayashi)
  • “Cross-Lingual Text Similarity Exploiting Neural Machine Translation Models.” Journal of Information Science, Vol. 47, No. 3, pp. 404-418, June 2021.
  • “Nowcasting Business Sentiment from Economic News Articles.” IPSJ Journal, Vol. 62, No. 5, pp. 1288-1297, May 2021. (In Japanese)
  • (with Yusuke Ikuta)

Academic Association Affiliations

  • Information Processing Society of Japan
  • The Japanese Society for Artificial Intelligence

Research Projects & Papers

  • SEKI, Kazuhiro

    Practical Application of S-APIR Utilizing Textual Data

    Research Projects

    Research Project » 2023FiscalYear » Economic forecast and analysis

    AUTHOR : 
    SEKI, Kazuhiro

    ABSTRACT

    Research leader:

    Kazuhiro Seki (Senior Researcher, APIR/Professor, Konan University)

     

    Research outline:

    With the rapid development of information technology in recent years, large-scale data generated by domestic and international economic activities are becoming available in a variety of forms. The use of data containing extremely rich information, such as text data, is thought to be effective in making more precise judgments and forecasts of macroeconomic conditions. In view of this, this project aims to develop an index using text data, which can be instrumental in discerning economic trends.

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