深层次标签辅助分类任务的层次标签文本分类方法

doi:10.3778/j.issn.1002-8331.2302-0237

摘要/Abstract

摘要： 层次标签文本分类是自然语言处理领域中一项具有挑战性的任务，每个文档需要被正确分类到对应具有层次结构的多个标签中。然而在标签集中，由于标签包含的语义信息不充分，同时被归类到深层次标签的文档数量过少，深层次标签训练不充分，导致显著的标签训练不平衡问题。基于此，提出了深层次标签辅助分类任务的层次标签文本分类方法（DLAC）。该方法提出了一种深层次标签辅助分类器，在标签语义增强的基础上有效利用文本特征与深层次标签对应的父标签结点（即浅层次标签的丰富特征）来提升深层次标签的分类性能。与11种算法在三个数据集上的对比实验结果表明，模型能够有效提升深层次标签的分类性能，并取得良好效果。

关键词: 层次标签文本分类, 标签层次结构, 全局标签分类通道, 深层次标签辅助分类通道

Abstract: Hierarchical label text classification is a challenging task in natural language processing, where each document needs to be correctly classified into multiple labels corresponding to a hierarchical structure. However, in the label set, the insufficient semantic information contained in the labels, along with the low number of documents classified into deep labels, inadequate training of deep-level labels leads to significant imbalance problems in label training. A two-channel hierarchical label text classification method with deep label assisted classification task (DLAC) is proposed to deal with the above challenges. The method proposes a deep-level label assisted classifier that effectively uses text features with deep-level labels corresponding to parent label nodes (i.e., rich features of shallow labels) to improve the classification performance of deep-level labels based on semantic enhancement of labels. Experimental results with eleven algorithms on three datasets demonstrate that the proposed model effectively improves the classification performance of deep-level labels and achieves better results.

Key words: hierarchical label text classification, label hierarchy, global label classification channel, deep label assisted classification channel

曹渝昆, 魏子越, 唐艺嘉, 金成坤, 李云峰. 深层次标签辅助分类任务的层次标签文本分类方法[J]. 计算机工程与应用, 2024, 60(10): 105-112.

CAO Yukun, WEI Ziyue, TANG Yijia, JIN Chengkun, LI Yunfeng. Hierarchical Label Text Classification Method with Deep Label Assisted Classification Task[J]. Computer Engineering and Applications, 2024, 60(10): 105-112.

参考文献

[1] HUANG W, CHEN E, LIU Q, et al. Hierarchical multi-label text classification: an attention-based recurrent network approach[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019: 1051-1060.
[2] XU L, TENG S, ZHAO R, et al. Hierarchical multi-label text classification with horizontal and vertical category correlations[C]//Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021: 2459-2468.
[3] ZHOU J, MA C, LONG D, et al. Hierarchy-aware global model for hierarchical text classification[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 5-10, 2020: 1106-1117.
[4] CHEN H, MA Q, LIN Z, et al. Hierarchy-aware label semantics matching network for hierarchical text classification[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 2021: 4370-4379.
[5] WEHRMANN J, CERRI R, BARROS R C. Hierarchical multi-label classification networks[C]//Proceedings of the 35th International Conference on Machine Learning, 2018: 5225-5234.
[6] ROJAS K R, BUSTAMANTE G, ONCEVAY A, et al. Efficient strategies for hierarchical text classification: external knowledge and auxiliary tasks[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Jul 5-10, 2020: 2252-2257.
[7] BAHDANAU D, CHO K, BENGIO Y. 2014. Neural machine translation by jointly learning to align and translate[J]. arXiv:1409.0473, 2014.
[8] MAO Y, TIAN J, HAN J, et al. Hierarchical text classification with reinforced label assignment[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 445-455.
[9] XIAO L, HUANG X, CHEN B, et al. Label-specific document representation for multi-label text classification[C]//Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 2019: 466-475.
[10] MA Q, YUAN C, ZHOU W et al. Label-specific dual graph neural network for multi-label text classification[C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021: 3855-3864.
[11] ZHOU P, QI Z, ZHENG S, et al. Text classification improved by integrating bidirectional LSTM with two-dimensional max pooling[J]. arXiv:1611.06639, 2016.
[12] KINGMA D P, BA J. Adam: a method for stochastic optimization[C]//Proceedings of the 3rd International Conference on Learning Representations, San Diego, May 7-9, 2015.
[13] KOWSARI K, BROWN D E, HEIDARYSAFA M, et al. HDLTex: hierarchical deep learning for text classification[C]//Proceedings of the 2017 16th IEEE International Conference on Machine Learning and Applications, 2017: 364-371.
[14] YANG P, SUN X, LI W, et al. SGM: sequence generation model for multi-label classification[J]. arXiv:1806.04822, 2018.
[15] ALY R, REMUS S, BIEMANN C. Hierarchical multi-label classification of text with capsule networks[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, 2019: 323-330.
[16] GOPAL S, YANG Y. Recursive regularization for large-scale classification with hierarchical and graphical dependencies[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2013: 257-265.
[17] DENG Z, PENG H, HE D, et al. HTCInfoMax: a global model for hierarchical text classification via information maximization[J]. arXiv:2104.05220, 2021.
[18] CHAIB R, AZIZI N, HAMMAMI N E, et al. GL-LSTM model for multi-label text classification of cardiovascular disease reports[C]//Proceedings of the 2022 2nd International Conference on Innovative Research in Applied Science, Engineering and Technology, 2022: 1-6.
[19] OZMEN M, ZHANG H, WANG P, et al. Multi-relation message passing for multi-label text classification[C]//Proceedings of the 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, 2022: 3583-3587.
[20] WANDEE W, SONGMUANG P. Hierarchical multi-label classification of library subject headings[C]//Proceedings of the 2022 International Conference on Cybernetics and Innovations, 2022: 1-5.