Stance Detection Attending External Knowledge from Wikipedia

• Kazuaki, Hanawa

• Akira, Sasaki

• Naoaki, Okazaki

• Kentaro, Inui

抄録

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.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.27(2019) (online)------------------------------

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.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.27(2019) (online)------------------------------

収録刊行物

• 情報処理学会論文誌データベース（TOD）

  情報処理学会論文誌データベース（TOD） 12 (3), 2019-07-17

キーワード

• [研究論文] natural language processing, opinion mining, stance detection, world knowledge

詳細情報

• CRID: 1050001338388014464
• NII論文ID: 170000150453
• NII書誌ID: AA11464847
• ISSN: 18827799
• Web Site: http://id.nii.ac.jp/1001/00198093/
• 本文言語コード: en
• 資料種別: article
• データソース種別:
  • IRDB
  • CiNii Articles