Integrating Semantic-Space Finetuning and Self-Training for Semi-Supervised Multi-label Text Classification

Zhewei Xu*, Mizuho Iwaihara

*この研究の対応する著者

研究成果: Conference contribution

抄録

To meet the challenge of lack of labeled data in document classification tasks, semi-supervised learning has been studied, in which unlabeled samples are also utilized for training. Self-training is one of the iconic strategies for semi-supervised learning, in which a classifier trains itself by its own predictions. However, self-training has been mostly applied to multi-class classification, and rarely applied to the multi-label scenario. In this paper, we propose a self-training-based approach for semi-supervised multi-label document classification, in which semantic-space finetuning is introduced and integrated into the self-training process. Newly discovered credible predictions are used not only for classifier finetuning, but also for semantic-space finetuning, which further benefit label propagation for exploring more credible predictions. Experimental results confirm the effectiveness of the proposed approach and show a satisfactory improvement over the baseline methods.

本文言語English
ホスト出版物のタイトルTowards Open and Trustworthy Digital Societies - 23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021, Proceedings
編集者Hao-Ren Ke, Chei Sian Lee, Kazunari Sugiyama
出版社Springer Science and Business Media Deutschland GmbH
ページ249-263
ページ数15
ISBN(印刷版)9783030916688
DOI
出版ステータスPublished - 2021
イベント23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021 - Virtual, Online
継続期間: 2021 12月 12021 12月 3

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
13133 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference23rd International Conference on Asia-Pacific Digital Libraries, ICADL 2021
CityVirtual, Online
Period21/12/121/12/3

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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