Supervised Approaches for Japanese Wikification

• Item type: Trans(1)
• 公開日: 2017-03-22
• タイトル: Supervised Approaches for Japanese Wikification
• タイトル / 言語: en
• タイトル: Supervised Approaches for Japanese Wikification
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [研究論文] named entity disambiguation, entity linking, Wikification, SVM
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Information Sciences, Tohoku University
• 著者所属: Graduate School of Information Sciences, Tohoku University
• 著者所属: Graduate School of Information Sciences, Tohoku University
• 著者所属: Graduate School of Information Sciences, Tohoku University
• 著者所属: Graduate School of Information Sciences, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Sciences, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Sciences, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Sciences, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Sciences, Tohoku University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Sciences, Tohoku University
• 著者名: Shuangshuang, Zhou; Naoaki, Okazaki; Koji, Matsuda; Ran, Tian; Kentaro, Inui
• 著者名(英): Shuangshuang, Zhou; Naoaki, Okazaki; Koji, Matsuda; Ran, Tian; Kentaro, Inui
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Wikification is the task of connecting mentions in texts to entities in a large-scale knowledge base, Wikipedia. In this paper, we present a pipeline system for Japanese Wikification that consists of two components, namely candidate generation and candidate ranking. We investigate several techniques for each component, using a recently developed Japanese Wikification corpus. For candidate generation, we find that a name dictionary using anchor texts of Wikipedia is more effective than other methods based on similarity of surface forms. For candidate ranking, we verify that a set of features used in English Wikification is effective in Japanese Wikification as well. In addition, by using a corpus that links mentions to Japanese Wikipedia entries instead of to English Wikipedia entries, we are able to acquire rich contextual information from Japanese Wikipedia articles, which leads to improvements for Japanese mention disambiguation. We take this advantage by exploring several embedding models that encode context information of Wikipedia entities. The experimental results demonstrate that they improve candidate ranking. We also report the effect of each feature in detail. To sum, our system achieves 81.60% accuracy, significantly outperforming the previous work.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.25(2017) (online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Wikification is the task of connecting mentions in texts to entities in a large-scale knowledge base, Wikipedia. In this paper, we present a pipeline system for Japanese Wikification that consists of two components, namely candidate generation and candidate ranking. We investigate several techniques for each component, using a recently developed Japanese Wikification corpus. For candidate generation, we find that a name dictionary using anchor texts of Wikipedia is more effective than other methods based on similarity of surface forms. For candidate ranking, we verify that a set of features used in English Wikification is effective in Japanese Wikification as well. In addition, by using a corpus that links mentions to Japanese Wikipedia entries instead of to English Wikipedia entries, we are able to acquire rich contextual information from Japanese Wikipedia articles, which leads to improvements for Japanese mention disambiguation. We take this advantage by exploring several embedding models that encode context information of Wikipedia entities. The experimental results demonstrate that they improve candidate ranking. We also report the effect of each feature in detail. To sum, our system achieves 81.60% accuracy, significantly outperforming the previous work.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.25(2017) (online); ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AA11464847
• 書誌情報: 情報処理学会論文誌データベース（TOD）; 巻 10, 号 1, 発行日 2017-03-22
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7799
• 出版者 / 言語: ja
• 出版者: 情報処理学会