Incremental Construction of Causal Network from News Articles

• Item type: Journal(1)
• 公開日: 2011-12-15
• タイトル: Incremental Construction of Causal Network from News Articles
• タイトル / 言語: en
• タイトル: Incremental Construction of Causal Network from News Articles
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: 特集：情報爆発時代におけるIT基盤技術
• 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: Corporate Software Engineering Center
• 著者所属: Graduate School of Informatics, Kyoto University
• 著者所属: Graduate School of Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Corporate Software Engineering Center
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics, Kyoto University
• 著者所属(英): en
• 著者所属(英): Graduate School of Informatics, Kyoto University
• 著者名: Hiroshi, Ishii; Qiang, Ma; Masatoshi, Yoshikawa
• 著者名(英): Hiroshi, Ishii; Qiang, Ma; Masatoshi, Yoshikawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: We propose a novel method for the incremental construction of causal networks to clarify the relationships among news events. We propose the Topic-Event Causal (TEC) model as a causal network model and an incremental constructing method based on it. In the TEC model, a causal relation is expressed using a directed graph and a vertex representing an event. A vertex contains structured keywords consisting of topic keywords and an SVO tuple. An SVO tuple, which consists of a tuple of subject,verb and object keywords represent the details of the event. To obtain a chain of causal relations, vertices representing a similar event need to be detected. We reduce the time taken to detect them by restricting the calculation to topics using topic keywords. We detect them on a concept level. We propose an identification method that identifies the sense of the keywords and introduce three semantic distance methods to compare keywords. Our method detects vertices representing similar events more precisely than conventional methods. We carried out experiments to validate the proposed methods.; ------------------------------; 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.20(2012) No.1 (online); DOI http://dx.doi.org/10.2197/ipsjjip.20.207; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: We propose a novel method for the incremental construction of causal networks to clarify the relationships among news events. We propose the Topic-Event Causal (TEC) model as a causal network model and an incremental constructing method based on it. In the TEC model, a causal relation is expressed using a directed graph and a vertex representing an event. A vertex contains structured keywords consisting of topic keywords and an SVO tuple. An SVO tuple, which consists of a tuple of subject,verb and object keywords represent the details of the event. To obtain a chain of causal relations, vertices representing a similar event need to be detected. We reduce the time taken to detect them by restricting the calculation to topics using topic keywords. We detect them on a concept level. We propose an identification method that identifies the sense of the keywords and introduce three semantic distance methods to compare keywords. Our method detects vertices representing similar events more precisely than conventional methods. We carried out experiments to validate the proposed methods.; ------------------------------; 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.20(2012) No.1 (online); DOI http://dx.doi.org/10.2197/ipsjjip.20.207; ------------------------------
• 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: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 52, 号 12, 発行日 2011-12-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764