| Project/Area Number |
26280079
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Partial Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Intelligent informatics
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
Okumura Manabu 東京工業大学, 科学技術創成研究院, 教授 (60214079)
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| Co-Investigator(Kenkyū-buntansha) |
白井 清昭 北陸先端科学技術大学院大学, 情報科学研究科, 准教授 (30302970)
平尾 努 日本電信電話株式会社NTTコミュニケーション科学基礎研究所, その他部局等, 研究員 (40396148)
森本 郁代 関西学院大学, 法学部, 教授 (40434881)
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| Project Period (FY) |
2014-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥15,990,000 (Direct Cost: ¥12,300,000、Indirect Cost: ¥3,690,000)
Fiscal Year 2016: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000)
Fiscal Year 2015: ¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2014: ¥7,020,000 (Direct Cost: ¥5,400,000、Indirect Cost: ¥1,620,000)
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| Keywords | ソーシャルメディア / 対話 / 意見分析 / 意見要約 / 議事録作成 |
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| Outline of Final Research Achievements |
For the technologies for extracting and analyzing opinions, we developed a method for judging the importance of opinions, and presented a real-time tweet selection system for TV news programs. The system developed in this study collects tweets purporting to a TV news program, and chooses an appropriate tweet every 10 seconds. For the technologies for summarizing opinions, we formulated a summarization task as a combinatorial optimization problem, in which the nested tree was trimmed without losing important content in the source document. We used both dependency between words and dependency between sentences by constructing a nested tree. Next, we proposed methods for controlling the output sequence length for neural encoder-decoder models. Furthermore, we worked on Japanese sentence compression by a similar approach as in English.
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