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Weakly Supervised Multi-Label Text Classification
https://ipsj.ixsq.nii.ac.jp/records/201552
https://ipsj.ixsq.nii.ac.jp/records/20155247997296-44ed-46d5-aca9-f964c05e9eab
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-DBS19170007.pdf (835.2 kB)
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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.
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| DBS:会員：¥0, DLIB:会員：¥0 | ||
| Item type | SIG Technical Reports(1) | |||||||||
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| 公開日 | 2019-12-16 | |||||||||
| タイトル | ||||||||||
| タイトル | Weakly Supervised Multi-Label Text Classification | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Weakly Supervised Multi-Label Text Classification | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||||
| 資源タイプ | technical report | |||||||||
| 著者所属 | ||||||||||
| Graduate School of Information, Production and Systems Waseda University | ||||||||||
| 著者所属 | ||||||||||
| Graduate School of Information, Production and Systems Waseda University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Information, Production and Systems Waseda University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Information, Production and Systems Waseda University | ||||||||||
| 著者名 |
Jiaqi, Feng
× Jiaqi, Feng
× Mizuho, Iwaihara
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| 著者名(英) |
Jiaqi, Feng
× Jiaqi, Feng
× Mizuho, Iwaihara
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 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 between labels at a finer granularity, which will be finetuned via self-training. Experiments show the improvement of our method when given weak supervisions. |
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| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | 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 between labels at a finer granularity, which will be finetuned via self-training. Experiments show the improvement of our method when given weak supervisions. |
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| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AN10112482 | |||||||||
| 書誌情報 |
研究報告データベースシステム（DBS） 巻 2019-DBS-170, 号 7, p. 1-6, 発行日 2019-12-16 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 2188-871X | |||||||||
| Notice | ||||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
