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        <title>基于不完全标注的自监督多标签文本分类(Self-Training With Incomplete Labeling For Multi-Label Text Classification)</title>
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        <namePart type="given">Junfei</namePart>
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            <title>Proceedings of the 22nd Chinese National Conference on Computational Linguistics</title>
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    <abstract>“多标签文本分类((Multi-Label Text Classification, MLTC)旨在从预定义的候选标签集合中选择一个或多个文本对应的类别,是自然语言处理C)旨在从预定义的候选标签集合中选择一个或多个文本对应的类别,是自然语言处理(Natural Language Processing,NLP)的一项基本任务。前人工作大多基于规范且全面的标注数据集,而这些规范数据集需要严格的质量控制,一般很难获取。在真实的标注过程中,难免会丢失掉一些相关标签,进而导致不完全标注问题。为此本文提出了一种基于局部标注的自监督框架(Partial Self-Training,PST),该框架利用教师模型自动地给大规模无标注数据打伪标签,同时给不完全标注数据补充缺失标签,最后再利用这些数据反向更新教师模型。在合成数据集和真实数据集上的实验表明,本文提出的PST框架兼容现有的各类多标签文本分类模型,并且可以缓解不完全标注数据对模型的影响。”</abstract>
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%0 Conference Proceedings
%T 基于不完全标注的自监督多标签文本分类(Self-Training With Incomplete Labeling For Multi-Label Text Classification)
%A Ren, Junfei
%A Zhu, Tong
%A Chen, Wenliang
%Y Sun, Maosong
%Y Qin, Bing
%Y Qiu, Xipeng
%Y Jiang, Jing
%Y Han, Xianpei
%S Proceedings of the 22nd Chinese National Conference on Computational Linguistics
%D 2023
%8 August
%I Chinese Information Processing Society of China
%C Harbin, China
%G Chinese
%F ren-etal-2023-ji
%X “多标签文本分类((Multi-Label Text Classification, MLTC)旨在从预定义的候选标签集合中选择一个或多个文本对应的类别,是自然语言处理C)旨在从预定义的候选标签集合中选择一个或多个文本对应的类别,是自然语言处理(Natural Language Processing,NLP)的一项基本任务。前人工作大多基于规范且全面的标注数据集,而这些规范数据集需要严格的质量控制,一般很难获取。在真实的标注过程中,难免会丢失掉一些相关标签,进而导致不完全标注问题。为此本文提出了一种基于局部标注的自监督框架(Partial Self-Training,PST),该框架利用教师模型自动地给大规模无标注数据打伪标签,同时给不完全标注数据补充缺失标签,最后再利用这些数据反向更新教师模型。在合成数据集和真实数据集上的实验表明,本文提出的PST框架兼容现有的各类多标签文本分类模型,并且可以缓解不完全标注数据对模型的影响。”
%U https://aclanthology.org/2023.ccl-1.2
%P 17-30

Markdown (Informal)

[基于不完全标注的自监督多标签文本分类(Self-Training With Incomplete Labeling For Multi-Label Text Classification)](https://aclanthology.org/2023.ccl-1.2) (Ren et al., CCL 2023)

• 基于不完全标注的自监督多标签文本分类(Self-Training With Incomplete Labeling For Multi-Label Text Classification) (Ren et al., CCL 2023)

ACL

• Junfei Ren, Tong Zhu, and Wenliang Chen. 2023. 基于不完全标注的自监督多标签文本分类(Self-Training With Incomplete Labeling For Multi-Label Text Classification). In Proceedings of the 22nd Chinese National Conference on Computational Linguistics, pages 17–30, Harbin, China. Chinese Information Processing Society of China.