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Active Learning With Complementary Sampling for Instructing Class-Biased Multi-Label Text Emotion Classification
https://tokushima-u.repo.nii.ac.jp/records/2011018
https://tokushima-u.repo.nii.ac.jp/records/2011018d18b5b94-a0d6-415a-bf61-b5303c5f7867
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
taffc_14_1_523.pdf (8.07 MB)
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| Item type | 文献 / Documents(1) | |||||||||||||||||||
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| 公開日 | 2023-07-21 | |||||||||||||||||||
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| アクセス権 | open access | |||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||
| 出版社版DOI | ||||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||||
| 関連識別子 | https://doi.org/10.1109/TAFFC.2020.3038401 | |||||||||||||||||||
| 言語 | ja | |||||||||||||||||||
| 関連名称 | 10.1109/TAFFC.2020.3038401 | |||||||||||||||||||
| 出版タイプ | ||||||||||||||||||||
| 出版タイプ | AM | |||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||||||||
| タイトル | ||||||||||||||||||||
| タイトル | Active Learning With Complementary Sampling for Instructing Class-Biased Multi-Label Text Emotion Classification | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 著者 |
康, 鑫
× 康, 鑫× Shi, Xuefeng
× 呉, 雨濃
× 任, 福継
WEKO
401
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| 抄録 | ||||||||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||||||||
| 内容記述 | High-quality corpora have been very scarce for the text emotion research. Existing corpora with multi-label emotion annotations have been either too small or too class-biased to properly support a supervised emotion learning. In this paper, we propose a novel active learning method for efficiently instructing the human annotations for a less-biased and high-quality multi-label emotion corpus. Specifically, to compensate annotation for the minority-class examples, we propose a complementary sampling strategy based on unlabeled resources by measuring a probabilistic distance between the expected emotion label distribution in a temporary corpus and an uniform distribution. Qualitative evaluations are also given to the unlabeled examples, in which we evaluate the model uncertainties for multi-label emotion predictions, their syntactic representativeness for the other unlabeled examples, and their diverseness to the labeled examples, for a high-quality sampling. Through active learning, a supervised emotion classifier gets progressively improved by learning from these new examples. Experiment results suggest that by following these sampling strategies we can develop a corpus of high-quality examples with significantly relieved bias for emotion classes. Compared to the learning procedures based on traditional active learning algorithms, our learning procedure indicates the most efficient learning curve and estimates the best multi-label emotion predictions. | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||
| 主題 | Active learning | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||
| 主題 | complementary sampling | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||
| 主題 | class-biased multi-label classification | |||||||||||||||||||
| キーワード | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 主題Scheme | Other | |||||||||||||||||||
| 主題 | text emotion | |||||||||||||||||||
| 書誌情報 |
en : IEEE Transactions on Affective Computing 巻 14, 号 1, p. 523-536, 発行日 2020-11-16 |
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| 収録物識別子タイプ | ISSN | |||||||||||||||||||
| 収録物識別子 | 19493045 | |||||||||||||||||||
| 出版者 | ||||||||||||||||||||
| 出版者 | IEEE | |||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 権利情報 | ||||||||||||||||||||
| 言語 | en | |||||||||||||||||||
| 権利情報 | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||||||
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| 識別子 | 372448 | |||||||||||||||||||
| 識別子タイプ | URI | |||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||
