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- Hanawa Kazuaki
- RIKEN Center for Advanced Intelligence Project Tohoku University
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- Sasaki Akira
- Recruit Technologies Co., Ltd.
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- Okazaki Naoaki
- Tokyo Institute of Technology
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- Inui Kentaro
- RIKEN Center for Advanced Intelligence Project Tohoku University
抄録
<p>This paper presents a novel approach to stance detection for unseen topics that takes advantage of external knowledge about the topics. We build a new stance detection dataset consisting of 6, 701 tweets on seven topics with associated Wikipedia articles. An analysis of this dataset confirms the necessity of external knowledge for this task. This paper also presents a method of extracting related concepts and events from Wikipedia articles. To incorporate this extracted knowledge into stance detection, we propose a novel neural network model that can attend to such related concepts and events when encoding the given text using bi-directional long short-term memories. Our experimental results demonstrate that the proposed method, using knowledge extracted from Wikipedia, can improve stance detection performance.</p>
収録刊行物
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- Journal of Information Processing
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Journal of Information Processing 27 (0), 499-506, 2019
一般社団法人 情報処理学会
- Tweet
詳細情報 詳細情報について
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- CRID
- 1390564238110222336
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- NII論文ID
- 130007690194
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- ISSN
- 18826652
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- Crossref
- CiNii Articles
- KAKEN
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- 抄録ライセンスフラグ
- 使用不可