| Project/Area Number |
18K11991
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 90020:Library and information science, humanistic and social informatics-related
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| Research Institution | Future University-Hakodate |
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Principal Investigator |
Murai Hajime 公立はこだて未来大学, システム情報科学部, 教授 (70452018)
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| Project Period (FY) |
2018-04-01 – 2023-03-31
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| Project Status |
Completed (Fiscal Year 2022)
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| Budget Amount *help |
¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000)
Fiscal Year 2022: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2021: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2020: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2019: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
Fiscal Year 2018: ¥520,000 (Direct Cost: ¥400,000、Indirect Cost: ¥120,000)
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| Keywords | 発話 / 文体 / 属性 / 物語 / 日常会話 / 話者 / 聴者 / 会話文 / 自称詞 / 対称詞 / 計量文体学 / 物語文 / 自然言語処理 / コーパス / 会話 / 登場人物 |
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| Outline of Final Research Achievements |
In Japanese utterances, even if the content is the same, the style of the utterance changes diversely under the influence of various attributes of speakers and listeners. Based on the corpus of Japanese narrative sentences developed by the applicant and the daily conversation corpus developed by the National Institute for Japanese Language and Linguistics, various attributes that are thought to affect the writing style are added to each utterance sentence. They were added as tags, and the main elements that make up the style of the utterances were analyzed using statistical methods. As a result, several basic stylistic patterns in Japanese utterances were extracted. In addition, the differences between the usages of these stylistic patterns depending various attributes and the differences between stories and daily conversations were also clarified.
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| Academic Significance and Societal Importance of the Research Achievements |
日本語における発話の文体と属性の関係が明らかになることで，コンピュータを用いた話者とその属性の自動推定や，より自然な対話型人工知能の開発などが可能になると期待される．またこれらの成果は国語教育や小説の執筆の補助などにおいても有効に活用可能であると考えられる．
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