{"metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00212729","sets":["1164:1165:10664:10665"]},"path":["10665"],"owner":"44499","recid":"212729","title":["Ensemble BERT-BiLSTM-CNN Model for Sequence Classification"],"pubdate":{"attribute_name":"公開日","attribute_value":"2021-09-09"},"_buckets":{"deposit":"520ece38-ee54-43ac-aaac-8a67214b9460"},"_deposit":{"id":"212729","pid":{"type":"depid","value":"212729","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Ensemble BERT-BiLSTM-CNN Model for Sequence Classification","author_link":["543182","543181","543180","543179"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Ensemble BERT-BiLSTM-CNN Model for Sequence Classification"},{"subitem_title":"Ensemble BERT-BiLSTM-CNN Model for Sequence Classification","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2021-09-09","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"National Institute of Informatics"},{"subitem_text_value":"National Institute of Informatics"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"National Institute of Informatics","subitem_text_language":"en"},{"subitem_text_value":"National Institute of Informatics","subitem_text_language":"en"}]},"item_language":{"attribute_name":"言語","attribute_value_mlt":[{"subitem_language":"eng"}]},"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/212729/files/IPSJ-DBS21173003.pdf","label":"IPSJ-DBS21173003.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DBS21173003.pdf","filesize":[{"value":"1.1 MB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"13"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"6b4358a7-4eee-429e-a5bf-5e91d202c8c3","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2021 by the Institute of Electronics, Information and Communication Engineers This SIG report is only available to those in membership of the SIG."}]},"item_4_creator_5":{"attribute_name":"著者名","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Vuong, Thi Hong"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsuhiro, Takasu"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Vuong, Thi Hong","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Atsuhiro, Takasu","creatorNameLang":"en"}],"nameIdentifiers":[{}]}]},"item_4_source_id_9":{"attribute_name":"書誌レコードID","attribute_value_mlt":[{"subitem_source_identifier":"AN10112482","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":"2188-871X","subitem_source_identifier_type":"ISSN"}]},"item_4_description_7":{"attribute_name":"論文抄録","attribute_value_mlt":[{"subitem_description":"Ensemble methods use multiple learning algorithms to obtain better predictive performance. Currently, deep learning models with multilayer processing architecture are showed that the performance is better than the traditional classification models. Ensemble deep learning models combine the advantages of both ensemble learning and deep learning such that the final model has better performance. This paper presents a novel ensemble deep learning method, achieving robust and effective sequence classification facing sparse data. We use the BERT (Bidirectional Encoder Representation from Transformers) as the word embedding method. Then, we integrate the BiLSTM (Bidirectional Long Short-Term Memory) and CNN (Convolutional Neural Network) with an attention mechanism for sequence classification. We evaluate our ensemble models with two datasets with the different baseline methods. The first dataset is from IMDB and contains 50,000 movie reviews, labeled with two sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments for six classes. The experimental results show that our proposed method provides an accurate, reliable, and effective solution for sequence data classification.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Ensemble methods use multiple learning algorithms to obtain better predictive performance. Currently, deep learning models with multilayer processing architecture are showed that the performance is better than the traditional classification models. Ensemble deep learning models combine the advantages of both ensemble learning and deep learning such that the final model has better performance. This paper presents a novel ensemble deep learning method, achieving robust and effective sequence classification facing sparse data. We use the BERT (Bidirectional Encoder Representation from Transformers) as the word embedding method. Then, we integrate the BiLSTM (Bidirectional Long Short-Term Memory) and CNN (Convolutional Neural Network) with an attention mechanism for sequence classification. We evaluate our ensemble models with two datasets with the different baseline methods. The first dataset is from IMDB and contains 50,000 movie reviews, labeled with two sentiment classes. The second dataset is the Toxic Comment Dataset with more than 150,000 comments for six classes. The experimental results show that our proposed method provides an accurate, reliable, and effective solution for sequence data classification.","subitem_description_type":"Other"}]},"item_4_biblio_info_10":{"attribute_name":"書誌情報","attribute_value_mlt":[{"bibliographicPageEnd":"6","bibliographic_titles":[{"bibliographic_title":"研究報告データベースシステム（DBS）"}],"bibliographicPageStart":"1","bibliographicIssueDates":{"bibliographicIssueDate":"2021-09-09","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"3","bibliographicVolumeNumber":"2021-DBS-173"}]},"relation_version_is_last":true,"weko_creator_id":"44499"},"id":212729,"updated":"2025-01-19T17:23:56.748127+00:00","links":{},"created":"2025-01-19T01:13:39.673651+00:00"}