{"updated":"2025-01-19T17:15:41.049024+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00213147","sets":["1164:4179:10535:10708"]},"path":["10708"],"owner":"44499","recid":"213147","title":["ハンドメイド作品を扱うECサイトに特化したBERTを用いた言語モデル構築に向けた取り組み"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-09-21"},"_buckets":{"deposit":"15133981-f44c-49a8-9714-0380dedf1b46"},"_deposit":{"id":"213147","pid":{"type":"depid","value":"213147","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"ハンドメイド作品を扱うECサイトに特化したBERTを用いた言語モデル構築に向けた取り組み","author_link":["544836","544834","544835"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"ハンドメイド作品を扱うECサイトに特化したBERTを用いた言語モデル構築に向けた取り組み"}]},"item_type_id":"4","publish_date":"2021-09-21","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"GMOペパボ株式会社ペパボ研究所／九州大学大学院システム情報科学府情報知能工学専攻"},{"subitem_text_value":"GMOペパボ株式会社ペパボ研究所"},{"subitem_text_value":"GMOペパボ株式会社ペパボ研究所"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Pepabo R&D Institute, GMO Pepabo, Inc. / Department of Advanced Information Technology, Graduate School of ISEE, Kyushu University","subitem_text_language":"en"},{"subitem_text_value":"Pepabo R&D Institute, GMO Pepabo, Inc.","subitem_text_language":"en"},{"subitem_text_value":"Pepabo R&D Institute, GMO Pepabo, Inc.","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/213147/files/IPSJ-NL21250005.pdf","label":"IPSJ-NL21250005.pdf"},"date":[{"dateType":"Available","dateValue":"2023-09-21"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-NL21250005.pdf","filesize":[{"value":"342.0 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":"8519ce44-da91-453a-830a-805e75895493","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"酒井, 敏彦"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"三宅, 悠介"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"栗林, 健太郎"}],"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":"自然言語処理の技術は，EC サイトで扱うテキストデータを対象とする，質問応答や商品の分類などのタスクに活用されている．ハンドメイド作品を扱う EC サイトにおける自然言語処理の課題は (1) 人手でタスクを解くのは困難，(2) ハンドメイド作品を扱う EC サイトの作品が多様，(3) ハンドメイド作品を扱う EC サイトの構造的な変化への追従が困難，の 3 つが挙げられる．本研究では，各課題に対して (1) 機械的にタスクを解くことができる，(2) 扱う作品が多様であっても作品の特徴を捉えられる，(3) 汎用的なモデルから fine-tuning することで構造的な変化へ追従可能，という理由から BERT+fine-tuning のモデルに着眼した．本報告では，ハンドメイド作品を扱う EC サイトの課題を含むタスクのうち，商品分類のタスクにおいて，比較評価を行った．ベースライン手法は従来から一般的に用いられる TF-IDF と分類器を用いた．結果として，上記の課題を解決し，BERT+fine-tuning のモデルが F1-score で良い分類性能であることを示した．今後は他のタスクへの応用を検討していく．","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"5","bibliographic_titles":[{"bibliographic_title":"研究報告自然言語処理（NL）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-09-21","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"5","bibliographicVolumeNumber":"2021-NL-250"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"created":"2025-01-19T01:14:03.481371+00:00","id":213147,"links":{}}