Abstract
This paper is concerned with the multi-label text classification (MLTC) task, whose goal is to assign one or more categorical labels to a document. The two critical characteristics of this task are the intra-class and inter-class information. The former means the distribution of samples belonging to the same category, and the latter models the relationships between labels such as label co-occurrence and label hierarchy. However, previous methods focus on either of them instead of combining both. This paper proposes a novel two-branch architecture to capture both intra-class and inter-class information. Experimental results show that considering both information improves the performance of the model. Besides, our model achieves competitive results on two widely used datasets.
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References
Adhikari, A., Ram, A., Tang, R., Lin, J.: Docbert: bert for document classification. arXiv preprint arXiv:1904.08398 (2019)
Chalkidis, I., Fergadiotis, M., Kotitsas, S., Malakasiotis, P., Aletras, N., Androutsopoulos, I.: An empirical study on large-scale multi-label text classification including few and zero-shot labels. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, 16–20 November 2020, pp. 7503–7515. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.emnlp-main.607
Chang, W.C., Yu, H.F., Zhong, K., Yang, Y., Dhillon, I.: X-bert: extreme multi-label text classification using bidirectional encoder representations from transformers. In: Proceedings of NeurIPS Science Meets Engineering of Deep Learning Workshop (2019)
Chen, Z.M., Wei, X.S., Wang, P., Guo, Y.: Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5177–5186 (2019)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Du, C., Chen, Z., Feng, F., Zhu, L., Gan, T., Nie, L.: Explicit interaction model towards text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6359–6366 (2019)
Fürnkranz, J., Hüllermeier, E., Mencía, E.L., Brinker, K.: Multilabel classification via calibrated label ranking. Mach. Learn. 73(2), 133–153 (2008)
Gopal, S., Yang, Y.: Multilabel classification with meta-level features. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 315–322 (2010)
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Twenty-ninth AAAI Conference on Artificial Intelligence (2015)
Lewis, D.D., Yang, Y., Rose, T.G., Li, F.: Rcv1: a new benchmark collection for text categorization research. J. Mach. Learn. Res. 5, 361–397 (2004)
Li, Q., Han, Z., Wu, X.M.: Deeper insights into graph convolutional networks for semi-supervised learning. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
Liu, J., Chang, W.C., Wu, Y., Yang, Y.: Deep learning for extreme multi-label text classification. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 115–124 (2017)
Liu, P., Qiu, X., Huang, X.: Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101 (2016)
Lu, J., Du, L., Liu, M., Dipnall, J.: Multi-label few/zero-shot learning with knowledge aggregated from multiple label graphs. In: Webber, B., Cohn, T., He, Y., Liu, Y. (eds.) Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, 16–20 November 2020, pp. 2935–2943. Association for Computational Linguistics (2020). https://doi.org/10.18653/v1/2020.emnlp-main.235
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)
Peng, H., Li, J., He, Y., Liu, Y., Bao, M., Wang, L., Song, Y., Yang, Q.: Large-scale hierarchical text classification with recursively regularized deep graph-cnn. In: Proceedings of the 2018 World Wide Web Conference, pp. 1063–1072 (2018)
Ren, Z., Peetz, M.H., Liang, S., Van Dolen, W., De Rijke, M.: Hierarchical multi-label classification of social text streams. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 213–222 (2014)
Rios, A., Kavuluru, R.: Few-shot and zero-shot multi-label learning for structured label spaces. In: Riloff, E., Chiang, D., Hockenmaier, J., Tsujii, J. (eds.) Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pp. 3132–3142. Association for Computational Linguistics (2018). https://doi.org/10.18653/v1/d18-1352
Xiao, L., Huang, X., Chen, B., Jing, L.: Label-specific document representation for multi-label text classification. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 466–475 (2019)
Yao, L., Mao, C., Luo, Y.: Graph convolutional networks for text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 7370–7377 (2019)
You, R., Zhang, Z., Wang, Z., Dai, S., Mamitsuka, H., Zhu, S.: Attentionxml: label tree-based attention-aware deep model for high-performance extreme multi-label text classification. arXiv preprint arXiv:1811.01727 (2018)
Zhang, W., Yan, J., Wang, X., Zha, H.: Deep extreme multi-label learning. In: Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, pp. 100–107 (2018)
Zhou, J., et al.: Hierarchy-aware global model for hierarchical text classification. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1106–1117 (2020)
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Wu, X., Ru, D., Zhang, W., Yu, Y., Feng, Z. (2023). Aggregating Intra-class and Inter-class Information for Multi-label Text Classification. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1791. Springer, Singapore. https://doi.org/10.1007/978-981-99-1639-9_20
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