Presentation 2022-12-21
Modality estimation of reflection texts written in In-house training
Makoto Yamada, Tsunenori Mine,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In this study, we propose a modality estimation method for the reflection texts written in in-house training programs. By estimating the semantics of the modality, we aim to understand the degree of understanding of the training content and the degree of growth of employees over time. Experimental results show that the proposed methods enables modality estimation with higher accuracy than manually formulated rules based on sentence structure, even when the modality is subdivided into four categories: probability, value judgment, attitude, and tense.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) In-house training / Reflection text / NLP / BERT
Paper # AI2022-37
Date of Issue 2022-12-14 (AI)

Conference Information
Committee AI
Conference Date 2022/12/21(1days)
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Place (in English)
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Topics (in English)
Chair Yuichi Sei(Univ. of Electro-Comm.)
Vice Chair Yuko Sakurai(AIST) / Tadachika Ozono(Nagoya Inst. of Tech.)
Secretary Yuko Sakurai(Tokyo Univ. of Agriculture and Technology) / Tadachika Ozono(Toho Univ.)
Assistant Kazutaka Matsuzaki(Chuo Univ.)

Paper Information
Registration To Technical Committee on Artificial Intelligence and Knowledge-Based Processing
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Modality estimation of reflection texts written in In-house training
Sub Title (in English)
Keyword(1) In-house training
Keyword(2) Reflection text
Keyword(3) NLP
Keyword(4) BERT
1st Author's Name Makoto Yamada
1st Author's Affiliation Kyushu University(Kyushu Univ.)
2nd Author's Name Tsunenori Mine
2nd Author's Affiliation Kyushu University(Kyushu Univ.)
Date 2022-12-21
Paper # AI2022-37
Volume (vol) vol.122
Number (no) AI-322
Page pp.pp.24-29(AI),
#Pages 6
Date of Issue 2022-12-14 (AI)