{"id":201552,"updated":"2025-01-19T20:59:00.507658+00:00","links":{},"created":"2025-01-19T01:04:48.725132+00:00","metadata":{"_oai":{"id":"oai:ipsj.ixsq.nii.ac.jp:00201552","sets":["1164:1165:9888:10036"]},"path":["10036"],"owner":"44499","recid":"201552","title":["Weakly Supervised Multi-Label Text Classification"],"pubdate":{"attribute_name":"公開日","attribute_value":"2019-12-16"},"_buckets":{"deposit":"388678c8-5043-4853-84a6-c5b68cb8f247"},"_deposit":{"id":"201552","pid":{"type":"depid","value":"201552","revision_id":0},"owners":[44499],"status":"published","created_by":44499},"item_title":"Weakly Supervised Multi-Label Text Classification","author_link":["493410","493408","493411","493409"],"item_titles":{"attribute_name":"タイトル","attribute_value_mlt":[{"subitem_title":"Weakly Supervised Multi-Label Text Classification"},{"subitem_title":"Weakly Supervised Multi-Label Text Classification","subitem_title_language":"en"}]},"item_type_id":"4","publish_date":"2019-12-16","item_4_text_3":{"attribute_name":"著者所属","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information, Production and Systems Waseda University"},{"subitem_text_value":"Graduate School of Information, Production and Systems Waseda University"}]},"item_4_text_4":{"attribute_name":"著者所属(英)","attribute_value_mlt":[{"subitem_text_value":"Graduate School of Information, Production and Systems Waseda University","subitem_text_language":"en"},{"subitem_text_value":"Graduate School of Information, Production and Systems Waseda University","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/201552/files/IPSJ-DBS19170007.pdf","label":"IPSJ-DBS19170007.pdf"},"format":"application/pdf","billing":["billing_file"],"filename":"IPSJ-DBS19170007.pdf","filesize":[{"value":"835.2 kB"}],"mimetype":"application/pdf","priceinfo":[{"tax":["include_tax"],"price":"0","billingrole":"13"},{"tax":["include_tax"],"price":"0","billingrole":"44"}],"accessrole":"open_login","version_id":"4dc39749-de20-47e1-9ab9-1197f62fff3c","displaytype":"detail","licensetype":"license_note","license_note":"Copyright (c) 2019 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":"Jiaqi, Feng"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Mizuho, Iwaihara"}],"nameIdentifiers":[{}]}]},"item_4_creator_6":{"attribute_name":"著者名(英)","attribute_type":"creator","attribute_value_mlt":[{"creatorNames":[{"creatorName":"Jiaqi, Feng","creatorNameLang":"en"}],"nameIdentifiers":[{}]},{"creatorNames":[{"creatorName":"Mizuho, Iwaihara","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":"Multi-label text classification is a variant of the classification problem where each document is associated with multiple labels, while traditional text classification aims to assign a single label to each document. There exist some previous studies on this problem that have made remarkable achievements, however, existing methods suffer from the lack of labeled data and tend to ignore the interdependencies among labels. In this paper, we propose a method to improve the performance of multilabel text classification given a small set of labeled data. Our method utilizes both the labeled data and unlabeled data to exploit coarse correlations between labels and generates pseudo documents to make up for the deficiency of labeled data, which are used to pre-train our model. Meanwhile, we apply a hierarchical attentional sequence generation model to capture the correlations\nbetween labels at a finer granularity, which will be finetuned via self-training. Experiments show the improvement of our method when given weak supervisions.","subitem_description_type":"Other"}]},"item_4_description_8":{"attribute_name":"論文抄録(英)","attribute_value_mlt":[{"subitem_description":"Multi-label text classification is a variant of the classification problem where each document is associated with multiple labels, while traditional text classification aims to assign a single label to each document. There exist some previous studies on this problem that have made remarkable achievements, however, existing methods suffer from the lack of labeled data and tend to ignore the interdependencies among labels. In this paper, we propose a method to improve the performance of multilabel text classification given a small set of labeled data. Our method utilizes both the labeled data and unlabeled data to exploit coarse correlations between labels and generates pseudo documents to make up for the deficiency of labeled data, which are used to pre-train our model. Meanwhile, we apply a hierarchical attentional sequence generation model to capture the correlations\nbetween labels at a finer granularity, which will be finetuned via self-training. Experiments show the improvement of our method when given weak supervisions.","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":"2019-12-16","bibliographicIssueDateType":"Issued"},"bibliographicIssueNumber":"7","bibliographicVolumeNumber":"2019-DBS-170"}]},"relation_version_is_last":true,"weko_creator_id":"44499"}}