{"id":77411,"updated":"2025-01-21T20:56:24.009443+00:00","links":{},"created":"2025-01-18T23:32:59.828037+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00077411","sets":["1164:4179:6308:6528"]},"path":["6528"],"owner":"10","recid":"77411","title":["述語項構造の共起情報と格フレームを用いた事態間知識の自動獲得"],"pubdate":{"attribute_name":"公開日","attribute_value":"2011-09-09"},"_buckets":{"deposit":"ad0dc8c2-c350-44ca-81e8-8814712c9bc3"},"_deposit":{"id":"77411","pid":{"type":"depid","value":"77411","revision_id":0},"owners":[10],"status":"published","created_by":10},"item_title":"述語項構造の共起情報と格フレームを用いた事態間知識の自動獲得","author_link":["0","0"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"述語項構造の共起情報と格フレームを用いた事態間知識の自動獲得"},{"subitem_title":"Acquiring Strongly-related Events using Predicate-argument Co-occurring Statistics and Caseframe","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"知識獲得・抽出","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2011-09-09","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"京都大学"},{"subitem_text_value":"京都大学"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Kyoto University","subitem_text_language":"en"},{"subitem_text_value":"Kyoto University","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_publisher":{"attribute_name":"出版者","attribute_value_mlt":[{"subitem_publisher":"情報処理学会","subitem_publisher_language":"ja"}]},"publish_status":"0","weko_shared_id":-1,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/77411/files/IPSJ-NL11203002.pdf"},"date":[{"dateType":"Available","dateValue":"2013-09-09"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-NL11203002.pdf","filesize":[{"value":"202.2 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"23"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"47cabe36-0182-43c0-b53b-3957cd0166b7","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2011 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"柴田, 知秀"},{"creatorName":"黒橋, 禎夫"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Tomohide, Shibata","creatorNameLang":"en"},{"creatorName":"Sadao, Kurohashi","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10115061","subitem_source_identifier_type":"NCID"}]},"item_4_textarea_12":{"attribute_name":"Notice","attribute_value_mlt":[{"subitem_textarea_value":"SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc."}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_18gh","resourcetype":"technical report"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本論文では述語項構造の共起情報と格フレームを用いることにより，大規模コーパスから事態間知識を獲得する手法について述べる．述語項構造の共起情報はアソシエーション分析を用いて効率的に計算し，述語に対する項の必須性の判断を行なう．そして，格フレームを用いて項のアライメントをとる．16 億文からなる Web コーパスを用いて実験を行なったところ，事態ペアの獲得精度が 96%，項のアライメント精度が 79.1%であり，獲得された事態ペアの数は約 2 万となった．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"This paper proposes a method for automatically acquiring strongly-related events from a large corpus using predicate-argument co-occurring statistics and caseframe. The co-occurrence measure is calculated using an association rule mining method, and the importance of an argument for each predicate-argument is judged. Then, the argument alignment in the pair of predicate-arguments is performed by using a caseframe. We conducted experiments using aWeb corpus consisting of 1.6G sentences. The accuracy for the extracted event pairs was 96%, and the accuracy of the argument alignment was 79.1%. The number of acquired event pairs was about 20 thousands.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"8","bibliographic_titles":[{"bibliographic_title":"研究報告自然言語処理（NL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2011-09-09","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"2","bibliographicVolumeNumber":"2011-NL-203"}]},"relation_version_is_last":true,"weko_creator_id":"10"}}