{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00219564","sets":["1164:5251:10854:11006"]},"path":["11006"],"owner":"44499","recid":"219564","title":["深層学習を用いた少量データセットによる景気動向分析"],"pubdate":{"attribute_name":"公開日","attribute_value":"2022-08-25"},"_buckets":{"deposit":"d359b9f0-ce22-4859-9ec8-b7d7486c39ac"},"_deposit":{"id":"219564","pid":{"type":"depid","value":"219564","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"深層学習を用いた少量データセットによる景気動向分析","author_link":["572771","572772","572769","572770","572768","572773"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"深層学習を用いた少量データセットによる景気動向分析"},{"subitem_title":"Economic Trend Prediction based on DNN with Fewshot dataset","subitem_title_language":"en"}]},"item_keyword":{"attribute_name":"キーワード","attribute_value_mlt":[{"subitem_subject":"情報通信システム・データ分析（DPS）","subitem_subject_scheme":"Other"}]},"item_type_id":"4","publish_date":"2022-08-25","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"法政大学大学院理工学研究科応用情報工学専攻"},{"subitem_text_value":"法政大学大学院理工学研究科応用情報工学専攻"},{"subitem_text_value":"法政大学大学院理工学研究科応用情報工学専攻"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Science and Engineering, the University of Hosei","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Engineering, the University of Hosei","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Science and Engineering, the University of Hosei","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/219564/files/IPSJ-EIP22097007.pdf","label":"IPSJ-EIP22097007.pdf"},"date":[{"dateType":"Available","dateValue":"2024-08-25"}],"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-EIP22097007.pdf","filesize":[{"value":"1.4 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"660","billingrole":"5"},{"tax":["include_tax"],"price":"330","billingrole":"6"},{"tax":["include_tax"],"price":"0","billingrole":"26"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_date","version_id":"2296a0dd-39d6-43fa-8ab2-ca28c7e2304f","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2022 by the Information Processing Society of Japan"}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"森田, 彩星"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"向, 琨"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"藤井, 章博"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Ayase, Morita","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Kun, Xiang","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Akihiro, Fujii","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AA11238429","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":"2188-8647","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"本論文では，自然言語処理の最近の手法を適用し，日銀が提供する短観に基づく景気文書を用いて景気動向予測することを目的とする．短観とは，日銀が四半期ごとに景気状況について企業にアンケート調査をし，その集計結果や分析結果を基に日本の経済を観測するものである．自然言語処理において，2015 年に Attention が登場し，現在もその技術を用いた多様な応用研究が行われている．本論文では景気予測に使用する深層学習モデルとして Attention の構造を持つ BERT モデルを利用し，従来の深層学習モデルとの比較検討を行った．また短観に基づく景気文書は 1 年に 4 回しか公表されないため，取得できる景気文書が不足しており十分な学習を行うことができないという問題がある．この問題を解決するため，BERT と同じく Attention の構造を持つ GPT-2 を利用して新たに文章を生成することにより，少量の景気文書から高い予測性能が得ることができた．","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"In this paper, the prediction method of economic trends is studied. The method uses the enterprise's quarterly prediction documents where we apply the latest natural language processing technique. The survey document is the so-called Tankan, the Japanese National Enterprise Quarterly Economic Survey conducted by the Bank of Japan. The Japanese economy is observed based on the aggregated results and analysis results of Tankan. The study's BERT model has an Attention structure for economic forecasting. We compared it with the conventional deep learning models. Since Tankan data is obtained only 4 times a year, the number of sentences to process in the learning model is not enough. To solve this problem, also Attention-based sentence generation scheme GPT-2 is applied. Consequently, our prediction matches the official indicator by the Bank of Japan.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"7","bibliographic_titles":[{"bibliographic_title":"研究報告電子化知的財産・社会基盤（EIP）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2022-08-25","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"7","bibliographicVolumeNumber":"2022-EIP-97"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":219564,"updated":"2025-01-19T14:50:59.374621+00:00","links":{},"created":"2025-01-19T01:19:38.214389+00:00"}