Abstract
Multi-label text classification (MLTC) is a fundamental task in the natural language processing field, with the objective of assigning the most relevant labels to each document. Previous research has neglected the distinctions between similar labels and has not taken all of the relationships in documents into account. Furthermore, certain hidden distant correlations among components may be missing with those approaches. To address the challenges, we propose a Label-related Adaptive Graph Construction (LAGC) method based on the attention mechanism which incorporates both word and label information to capture label-related components from documents. Additionally, LAGC constructs local and global dynamic adaptive graph networks to effectively model the interactions among components. Simultaneously, a gated fusion module is implemented to integrate the outputs of these two graph networks, thereby obtaining the final representation of documents and subsequently constructing the multi-label text classifier. Extensive experimental results on two benchmark datasets illustrate that the LAGC method significantly outperforms all the baselines.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Disclosure of Interests
The authors have no competing interests to declare that are relevant to the content of this article.
References
Alswaidan, N., Menai, M.E.B.: A survey of state-of-the-art approaches for emotion recognition in text. Knowl. Inf. Syst. 62, 2937–2987 (2020)
Cai, L., Song, Y., Liu, T., Zhang, K.: A hybrid BERT model that incorporates label semantics via adjustive attention for multi-label text classification. IEEE Access 8, 152183–152192 (2020)
Chen, Z.M., Wei, X.S., Wang, P., Guo, Y.: Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5177–5186 (2019)
Fan, C., Chen, W., Tian, J., Li, Y., He, H., Jin, Y.: Accurate use of label dependency in multi-label text classification through the lens of causality. Appl. Intell., 1–17 (2023)
Kenton, J.D.M.W.C., Toutanova, L.K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, vol. 1, p. 2 (2019)
Kurata, G., Xiang, B., Zhou, B.: Improved neural network-based multi-label classification with better initialization leveraging label co-occurrence. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 521–526 (2016)
Lewis, D.D., Yang, Y., Russell-Rose, T., Li, F.: RCV1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)
Liu, H., Chen, G., Li, P., Zhao, P., Wu, X.: Multi-label text classification via joint learning from label embedding and label correlation. Neurocomputing 460, 385–398 (2021)
Liu, N., Wang, Q., Ren, J.: Label-embedding bi-directional attentive model for multi-label text classification. Neural. Process. Lett. 53, 375–389 (2021)
Ma, Q., Yuan, C., Zhou, W., Hu, S.: Label-specific dual graph neural network for multi-label text classification. In: 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), pp. 3855–3864 (2021)
Onan, A.: Bidirectional convolutional recurrent neural network architecture with group-wise enhancement mechanism for text sentiment classification. J. King Saud Univ. Comput. Inf. Sci. 34(5), 2098–2117 (2022)
Wang, G., et al.: Joint embedding of words and labels for text classification. arXiv preprint arXiv:1805.04174 (2018)
Wang, R., Ridley, R., Qu, W., Dai, X., et al.: A novel reasoning mechanism for multi-label text classification. Inf. Process. Manag. 58(2), 102441 (2021)
Wu, Z., Jain, P., Wright, M., Mirhoseini, A., Gonzalez, J.E., Stoica, I.: Representing long-range context for graph neural networks with global attention. In: Advances in Neural Information Processing Systems, vol. 34, pp. 13266–13279 (2021)
Xun, G., Jha, K., Sun, J., Zhang, A.: Correlation networks for extreme multi-label text classification. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1074–1082 (2020)
Yang, P., Luo, F., Ma, S., Lin, J., Sun, X.: A deep reinforced sequence-to-set model for multi-label classification. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5252–5258 (2019)
Yang, P., Sun, X., Li, W., Ma, S., Wu, W., Wang, H.: SGM: sequence generation model for multi-label classification. arXiv preprint arXiv:1806.04822 (2018)
Yao, Y., Gu, B., Su, Z., Guizani, M.: MVSTGN: a multi-view spatial-temporal graph network for cellular traffic prediction. IEEE Trans. Mob. Comput. (2021)
Yilmaz, S.F., Kaynak, E.B., Koç, A., Dibeklioğlu, H., Kozat, S.S.: Multi-label sentiment analysis on 100 languages with dynamic weighting for label imbalance. IEEE Trans. Neural Netw. Learn. Syst. (2021)
Zhang, X., Zhang, Q.W., Yan, Z., Liu, R., Cao, Y.: Enhancing label correlation feedback in multi-label text classification via multi-task learning. arXiv preprint arXiv:2106.03103 (2021)
Acknowledgments
The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. This work is supported by National Natural Science Foundation of China under Grant No.61971057.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zhou, X., Xie, X., Zhao, C., Yao, L., Li, Z., Zhang, Y. (2024). Label-Related Adaptive Graph Construction Based on Attention for Multi-label Text Classification. In: Huang, DS., Si, Z., Zhang, C. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science(), vol 14878. Springer, Singapore. https://doi.org/10.1007/978-981-97-5672-8_17
Download citation
DOI: https://doi.org/10.1007/978-981-97-5672-8_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-5671-1
Online ISBN: 978-981-97-5672-8
eBook Packages: Computer ScienceComputer Science (R0)