{"created":"2025-01-19T01:29:42.904438+00:00","links":{},"updated":"2025-01-19T11:16:34.004851+00:00","id":230135,"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00230135","sets":["6504:11436:11440"]},"path":["11440"],"owner":"44499","recid":"230135","title":["日本語 BERT モデルを用いたレビュアーの心情抽出"],"pubdate":{"attribute_name":"公開日","attribute_value":"2023-02-16"},"_buckets":{"deposit":"c2cbabd7-7c2f-49d0-b25f-2045d22682a4"},"_deposit":{"id":"230135","pid":{"type":"depid","value":"230135","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"日本語 BERT モデルを用いたレビュアーの心情抽出","author_link":["619146"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"日本語 BERT モデルを用いたレビュアーの心情抽出"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"人工知能と認知科学","subitem_subject_scheme":"Other"}]},"item_type_id":"22","publish_date":"2023-02-16","item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"jpn"}]},"item_22_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"愛知県大"}]},"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/230135/files/IPSJ-Z85-1V-07.pdf","label":"IPSJ-Z85-1V-07.pdf"},"date":[{"dateType":"Available","dateValue":"2023-11-17"}],"format":"application/pdf","filename":"IPSJ-Z85-1V-07.pdf","filesize":[{"value":"213.5 kB"}],"mimetype":"application/pdf","accessrole":"open_date","version_id":"4361cbbc-bb37-4f35-8ab9-c9cdc8583b23","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2023 by the Information Processing Society of Japan"}]},"item_22_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"中田, 聖之"}],"nameIdentifiers":[{}]}]},"item_resource_type":{"attribute_name":"資源タイプ","attribute_value_mlt":[{"resourceuri":"http://purl.org/coar/resource_type/c_5794","resourcetype":"conference paper"}]},"item_22_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN00349328","subitem_source_identifier_type":"NCID"}]},"item_22_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本研究では，商品レビュー文からレビュアーの心情を抽出するタスクに対して，BERTの各機構がタスクの精度向上のためにどのような役割を果たしているのかを明らかにすること目的とする．分析対象は，心情カテゴリをラベル付けした5000件のAmazon.comのレビュー文である．そして，東北大学の日本語BERTモデルをはじめとする様々な条件の機械学習モデルで学習を行う実験をした．実験結果から，BERTのPositional Embeddingが単語の語順を考慮し，Transformer Eocnderが文脈理解の精度を強めることがわかった．さらに，BERTには言葉の裏を読む性能に限界があることもわかった．","subitem_description_type":"Other"}]},"item_22_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"692","bibliographic_titles":[{"bibliographic_title":"第85回全国大会講演論文集"}],"bibliographicPageStart":"691","bibliographicIssueDates":{"bibliographicIssueDate":"2023-02-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"1","bibliographicVolumeNumber":"2023"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}