{"_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"}, "item_7_alternative_title_1": {"attribute_name": "\u305d\u306e\u4ed6\u306e\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_alternative_title": "Extracting Causal Relation using Event Coreference"}]}, "item_7_biblio_info_7": {"attribute_name": "\u66f8\u8a8c\u60c5\u5831", "attribute_value_mlt": [{"bibliographicIssueDates": {"bibliographicIssueDate": "2012-03-22", "bibliographicIssueDateType": "Issued"}, "bibliographic_titles": [{}]}]}, "item_7_date_granted_25": {"attribute_name": "\u5b66\u4f4d\u6388\u4e0e\u5e74\u6708\u65e5", "attribute_value_mlt": [{"subitem_dategranted": "2012-03-22"}]}, "item_7_degree_name_20": {"attribute_name": "\u5b66\u4f4d\u540d", "attribute_value_mlt": [{"subitem_degreename": "\u4fee\u58eb(\u60c5\u5831\u7406\u5de5\u5b66)"}]}, "item_7_description_5": {"attribute_name": "\u6284\u9332", "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\"\u2192\"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": "\u8457\u8005\u5225\u540d", "attribute_value_mlt": [{"nameIdentifiers": [{"nameIdentifier": "8199", "nameIdentifierScheme": "WEKO"}], "names": [{"name": "Tanaka, Shohei"}]}]}, "item_7_select_21": {"attribute_name": "\u5b66\u4f4d", "attribute_value_mlt": [{"subitem_select_item": "master"}]}, "item_7_subject_13": {"attribute_name": "\u65e5\u672c\u5341\u9032\u5206\u985e\u6cd5", "attribute_value_mlt": [{"subitem_subject": "007", "subitem_subject_scheme": "NDC"}]}, "item_7_text_24": {"attribute_name": "\u7814\u7a76\u79d1\u30fb\u5c02\u653b", "attribute_value_mlt": [{"subitem_text_value": "\u60c5\u5831\u7406\u5de5\u5b66\u7cfb\u7814\u7a76\u79d1\u96fb\u5b50\u60c5\u5831\u5b66\u5c02\u653b"}]}, "item_7_text_36": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"subitem_text_value": "Thesis"}]}, "item_7_text_4": {"attribute_name": "\u8457\u8005\u6240\u5c5e", "attribute_value_mlt": [{"subitem_text_value": "\u6771\u4eac\u5927\u5b66\u5927\u5b66\u9662\u60c5\u5831\u7406\u5de5\u5b66\u7cfb\u7814\u7a76\u79d1\u96fb\u5b50\u60c5\u5831\u5b66\u5c02\u653b"}, {"subitem_text_value": "Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo"}]}, "item_creator": {"attribute_name": "\u8457\u8005", "attribute_type": "creator", "attribute_value_mlt": [{"creatorNames": [{"creatorName": "\u7530\u4e2d, \u7fd4\u5e73"}], "nameIdentifiers": [{"nameIdentifier": "8198", "nameIdentifierScheme": "WEKO"}]}]}, "item_files": {"attribute_name": "\u30d5\u30a1\u30a4\u30eb\u60c5\u5831", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_date", "date": [{"dateType": "Available", "dateValue": "2017-05-31"}], "displaytype": "detail", "download_preview_message": "", "file_order": 0, "filename": "48106422.pdf", "filesize": [{"value": "763.5 kB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 763500.0, "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": "\u8a00\u8a9e", "attribute_value_mlt": [{"subitem_language": "jpn"}]}, "item_resource_type": {"attribute_name": "\u8cc7\u6e90\u30bf\u30a4\u30d7", "attribute_value_mlt": [{"resourcetype": "thesis", "resourceuri": "http://purl.org/coar/resource_type/c_46ec"}]}, "item_title": "\u30a4\u30d9\u30f3\u30c8\u5171\u53c2\u7167\u95a2\u4fc2\u3092\u5229\u7528\u3057\u305f\u56e0\u679c\u95a2\u4fc2\u77e5\u8b58\u306e\u7372\u5f97", "item_titles": {"attribute_name": "\u30bf\u30a4\u30c8\u30eb", "attribute_value_mlt": [{"subitem_title": "\u30a4\u30d9\u30f3\u30c8\u5171\u53c2\u7167\u95a2\u4fc2\u3092\u5229\u7528\u3057\u305f\u56e0\u679c\u95a2\u4fc2\u77e5\u8b58\u306e\u7372\u5f97"}]}, "item_type_id": "7", "owner": "1", "path": ["9/233/234", "34/105/262"], "permalink_uri": "http://hdl.handle.net/2261/51736", "pubdate": {"attribute_name": "\u516c\u958b\u65e5", "attribute_value": "2012-05-29"}, "publish_date": "2012-05-29", "publish_status": "0", "recid": "3412", "relation": {}, "relation_version_is_last": true, "title": ["\u30a4\u30d9\u30f3\u30c8\u5171\u53c2\u7167\u95a2\u4fc2\u3092\u5229\u7528\u3057\u305f\u56e0\u679c\u95a2\u4fc2\u77e5\u8b58\u306e\u7372\u5f97"], "weko_shared_id": null}