{"created":"2025-02-14T09:38:19.031651+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:02000216","sets":["1164:4961:1739501972671:1739502147705"]},"path":["1739502147705"],"owner":"80578","recid":"2000216","title":["青空文庫ModernBERTモデルによる国語研長単位係り受け解析"],"pubdate":{"attribute_name":"PubDate","attribute_value":"2025-02-01"},"_buckets":{"deposit":"97f473de-8f3c-424f-b063-b56110135ff3"},"_deposit":{"id":"2000216","pid":{"type":"depid","value":"2000216","revision_id":0},"owners":[80578],"status":"published","created_by":80578},"item_title":"青空文庫ModernBERTモデルによる国語研長単位係り受け解析","author_link":[],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"青空文庫ModernBERTモデルによる国語研長単位係り受け解析","subitem_title_language":"ja"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2025-02-01","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"京都大学"}]},"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/2000216/files/IPSJ-CH25137010.pdf","label":"IPSJ-CH25137010.pdf"},"date":[{"dateType":"Available","dateValue":"2027-02-01"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-CH25137010.pdf","filesize":[{"value":"996.8 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":"24"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"9fa56c4e-2613-47f3-b5e6-b696e87ad7f4","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2025 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"安岡,孝一"}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN1010060X","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":"2188-8957","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"2024年12月に発表されたModernBERTは，入出力幅8192トークンを，1.5億パラメータのモデルで実現している．これまでBERTやDeBERTaの1.5億パラメータ・モデルは，入出力幅が512トークン程度だったことに較べれば，格段の進歩である．係り受け解析での隣接確率行列を考えると，8192トークンもあれば90×90の正方行列がそのままモデルに乗ってしまう．三角行列に圧縮できれば，126×126までは乗りそうである．つまり，隣接確率行列をモデルに乗せてしまった形での解析アルゴリズムを，開発可能だということである．そのようなアルゴリズムを乗せた日本語ModernBERTは，本当に実現可能なのか．本稿では，その可能性を探る．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告人文科学とコンピュータ（CH）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2025-02-01","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"10","bibliographicVolumeNumber":"2025-CH-137"}]},"relation_version_is_last":true,"weko_creator_id":"80578"},"id":2000216,"updated":"2025-02-14T09:38:23.866784+00:00","links":{}}