{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00235108","sets":["1164:4179:11560:11676"]},"path":["11676"],"owner":"44499","recid":"235108","title":["大規模言語モデルを利用した語義定義文からの用例の自動構築"],"pubdate":{"attribute_name":"公開日","attribute_value":"2024-06-21"},"_buckets":{"deposit":"7a8bd4e9-29ca-46f6-8c68-d5324f14a463"},"_deposit":{"id":"235108","pid":{"type":"depid","value":"235108","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"大規模言語モデルを利用した語義定義文からの用例の自動構築","author_link":["641903","641901","641902","641906","641904","641900","641905","641907"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"大規模言語モデルを利用した語義定義文からの用例の自動構築"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"言語資源 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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":44499,"item_file_price":{"attribute_name":"Billing file","attribute_type":"file","attribute_value_mlt":[{"url":{"url":"https://ipsj.ixsq.nii.ac.jp/record/235108/files/IPSJ-NL24260017.pdf","label":"IPSJ-NL24260017.pdf"},"date":[{"dateType":"Available","dateValue":"2026-06-21"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-NL24260017.pdf","filesize":[{"value":"1.3 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Shibayama","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kanako, Komiya","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Masayuki, Asahara","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Hiroyuki, Shinnou","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_source_id_11":{"attribute_name":"ISSN","attribute_value_mlt":[{"subitem_source_identifier":"2188-8779","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"単語の用例は辞書編纂や語学学習などに有益である．しかしその構築は対象の単語が多義語である場合，用例内のその単語の語義が不明であるため，困難である．本論文では大規模言語モデルに辞書中の語義定義文を与えることで，その単語がその語義として使われる用例を生成させる．これによって多義語である単語の各語義に対する用例が構築できる．実験では辞書として岩波国語辞典，大規模言語モデルとして GPT-4o を利用した．構築した用例は SemEval2 の日本語辞書タスクを用いて評価した．具体的には SemEval2 の訓練データから学習したモデル（Base-model）と生成した用例から学習したモデル（LLM-model）を比較することで，生成した用例の評価を行った．LLM-model の正解率は低かったが，教師なしの枠組みである点，構築した用例を Data Augmentation として利用したモデルは Base-model よりも性能が高かった点などから生成した用例は有用だと考えられる．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告自然言語処理（NL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2024-06-21","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"17","bibliographicVolumeNumber":"2024-NL-260"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":235108,"updated":"2025-01-19T09:36:19.964169+00:00","links":{},"created":"2025-01-19T01:37:06.552706+00:00"}