Topic Selection Using Conceptual Distance: How to Select Topics that are Interesting but Unfamiliar to Users

カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門（英文）

発行日: 2023/07/01

タイトル(英語): Topic Selection Using Conceptual Distance: How to Select Topics that are Interesting but Unfamiliar to Users

著者名: Yuya Sakai (University of Electro-Communications), Mitsuharu Matsumoto (University of Electro-Communications)

著者名(英語): Yuya Sakai (University of Electro-Communications), Mitsuharu Matsumoto (University of Electro-Communications)

キーワード: topic selection，dialog system，conceptual distance，thesaurus dictionary

要約(英語): In this study, we established a topic selection method that recommends topics that are interesting and unfamiliar to users. To achieve this aim, we used conceptual distance to identify topics that were unfamiliar to users and improved the accuracy of this method by removing conceptually similar words. Many words used in conversations are excluded in the dictionaries and thesauruses. Thus, we developed a model for conceptual distance measurement using machine learning to measure conceptual distances even for such words. By conducting the subject experiments, we confirmed that the established system recommends topics a user is interested in but unfamiliar with compared with the baseline method developed in a previous research.

本誌: IEEJ Journal of Industry Applications Vol.12 No.4 （2023） Special Issue on “IPEC-Himeji 2022”

本誌掲載ページ: 588-595 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejjia/12/4/12_22006784/_article/-char/ja/