Presentation 2022-07-08
Topic Classification of Kyutech Corpus by Machine Learning
Shinnosuke Kawasaki, Kazutaka Shimada,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Discussion summarization is one of the most important tasks for discussion analysis. Utterances in a discussion contains several topics, and the topics have an important role for the summarization. In this paper, we report a topic classification task of utterances in a multi-party discussion corpus: Kyutech corpus. In the corpus, each utterance contains one to three topic tags. We compare several machine learning methods for the topic tag classification task.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Topic classification / Multi-label classification / Machine learning / Argument mining
Paper # NLC2022-3
Date of Issue 2022-07-01 (NLC)

Conference Information
Committee NLC / IPSJ-ICS
Conference Date 2022/7/8(1days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English) Application of natural language processing and intelligent systems, and general topic of NLP
Chair Mitsuo Yoshida(Toyohashi Univ. of Tech.)
Vice Chair Hiroki Sakaji(Univ. of Tokyo) / Takeshi Kobayakawa(NHK)
Secretary Hiroki Sakaji(NTT) / Takeshi Kobayakawa(Hiroshima Univ. of Economics)
Assistant Kanjin Takahashi(Sansan) / Yasuhiro Ogawa(Nagoya Univ.)

Paper Information
Registration To Technical Committee on Natural Language Understanding and Models of Communication / Special Interest Group on Intelligence and Complex Systems
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Topic Classification of Kyutech Corpus by Machine Learning
Sub Title (in English)
Keyword(1) Topic classification
Keyword(2) Multi-label classification
Keyword(3) Machine learning
Keyword(4) Argument mining
1st Author's Name Shinnosuke Kawasaki
1st Author's Affiliation Kyushu Institute of Technology(Kyutech)
2nd Author's Name Kazutaka Shimada
2nd Author's Affiliation Kyushu Institute of Technology(Kyutech)
Date 2022-07-08
Paper # NLC2022-3
Volume (vol) vol.122
Number (no) NLC-99
Page pp.pp.13-18(NLC),
#Pages 6
Date of Issue 2022-07-01 (NLC)