{"created":"2021-03-01T06:20:03.170177+00:00","id":3412,"links":{},"metadata":{"_buckets":{"deposit":"a5570610-f805-4e6b-b58a-580267bc5e50"},"_deposit":{"id":"3412","owners":[],"pid":{"revision_id":0,"type":"depid","value":"3412"},"status":"published"},"_oai":{"id":"oai:repository.dl.itc.u-tokyo.ac.jp:00003412","sets":["34:105:262","9:233:234"]},"item_7_alternative_title_1":{"attribute_name":"その他のタイトル","attribute_value_mlt":[{"subitem_alternative_title":"Extracting Causal Relation using Event Coreference"}]},"item_7_biblio_info_7":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicIssueDates":{"bibliographicIssueDate":"2012-03-22","bibliographicIssueDateType":"Issued"},"bibliographic_titles":[{}]}]},"item_7_date_granted_25":{"attribute_name":"学位授与年月日","attribute_value_mlt":[{"subitem_dategranted":"2012-03-22"}]},"item_7_degree_name_20":{"attribute_name":"学位名","attribute_value_mlt":[{"subitem_degreename":"修士(情報理工学)"}]},"item_7_description_5":{"attribute_name":"抄録","attribute_value_mlt":[{"subitem_description":"Causal relations are essential knowledge for interpreting discourse structure of text. This paper presents a method that extracts causal relations of lexical patterns in the form of quasi Horn clauses: for example, \"A acquires B\" AND \"B is located in X\"→\"the acquisition gives A operations in X\". The input of the method is a relation tuple consisting of two entities and a verb, e.g., (A, acquire, B). The method finds coreferencial expressions of the given relation that mention the same event. We use nominal forms of verbs included in FrameNet for finding the coreference expression. If a nominal form of a relation occurs as the subject in the dependency tree of a sentence, the sentence is likely to describe what is caused by the event. Then the method uses several NLP techniques (part-of-speech tagging, coreference resolution, dependency parsing and named entity recognition) in order to build a lexical pattern containing the entities (A and B). However, such rules are so specific that computers cannot reuse the knowledge of causality relation: for example, \"the acquisition gives A operations in Nevada\". Therefore, the proposed method generalize the rules by introducing a variable X and estimating the relation between X and A or between X and B. For evaluation, we asked human annotators to judge the correctness of the causal-relation rules. The result shows that the proposed method precisely extracts the causal-relation rules.","subitem_description_type":"Abstract"}]},"item_7_full_name_3":{"attribute_name":"著者別名","attribute_value_mlt":[{"nameIdentifiers":[{"nameIdentifier":"8199","nameIdentifierScheme":"WEKO"}],"names":[{"name":"Tanaka, Shohei"}]}]},"item_7_select_21":{"attribute_name":"学位","attribute_value_mlt":[{"subitem_select_item":"master"}]},"item_7_subject_13":{"attribute_name":"日本十進分類法","attribute_value_mlt":[{"subitem_subject":"007","subitem_subject_scheme":"NDC"}]},"item_7_text_24":{"attribute_name":"研究科・専攻","attribute_value_mlt":[{"subitem_text_value":"情報理工学系研究科電子情報学専攻"}]},"item_7_text_4":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"東京大学大学院情報理工学系研究科電子情報学専攻"},{"subitem_text_value":"Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo"}]},"item_creator":{"attribute_name":"著者","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"田中, 翔平"}],"nameIdentifiers":[{"nameIdentifier":"8198","nameIdentifierScheme":"WEKO"}]}]},"item_files":{"attribute_name":"ファイル情報","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_date","date":[{"dateType":"Available","dateValue":"2017-05-31"}],"displaytype":"detail","filename":"48106422.pdf","filesize":[{"value":"763.5 kB"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"label":"48106422.pdf","url":"https://repository.dl.itc.u-tokyo.ac.jp/record/3412/files/48106422.pdf"},"version_id":"bc5decbb-2d4e-465d-90ce-bb2d5755dfe2"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourcetype":"thesis","resourceuri":"http://purl.org/coar/resource_type/c_46ec"}]},"item_title":"イベント共参照関係を利用した因果関係知識の獲得","item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"イベント共参照関係を利用した因果関係知識の獲得"}]},"item_type_id":"7","owner":"1","path":["234","262"],"pubdate":{"attribute_name":"公開日","attribute_value":"2012-05-29"},"publish_date":"2012-05-29","publish_status":"0","recid":"3412","relation_version_is_last":true,"title":["イベント共参照関係を利用した因果関係知識の獲得"],"weko_creator_id":"1","weko_shared_id":null},"updated":"2022-12-19T03:45:04.163516+00:00"}