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Dual-view graph convolutional network for multi-label text classification

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Abstract

Multi-label text classification refers to assigning multiple relevant category labels to each text, which has been widely applied in the real world. To enhance the performance of multi-label text classification, most existing methods only focus on optimizing document and label representations, assuming accurate label-document similarity is crucial. However, whether the potential relevance between labels and if the problem of the long-tail distribution of labels could be solved are also key factors affecting the performance of multi-label classification. To this end, we propose a multi-label text classification model called DV-MLTC, which is based on a dual-view graph convolutional network to predict multiple labels for text. Specifically, we utilize graph convolutional neural networks to explore the potential correlation between labels in both the global and local views. First, we capture the global consistency of labels on the global label graph based on existing statistical information and generate label paths through a random walk algorithm to reconstruct the label graph. Then, to capture relationships between low-frequency co-occurring labels on the reconstructed graph, we guide the generation of reasonable co-occurring label pairs within the local neighborhood by utilizing the local consistency of labels, which also helps alleviate the long-tail distribution of labels. Finally, we integrate the global and local consistency of labels to address the problem of highly skewed distribution caused by incomplete label co-occurrence patterns in the label co-occurrence graph. The Evaluation shows that our proposed model achieves competitive results compared to existing state-of-the-art methods. Moreover, our model achieves a better balance between efficiency and performance.

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Availability of Data and Materials

The datasets analyzed during the current study were all derived from the following public domain resources. [AAPD: https://git.uwaterloo.ca/jimmylin/Castor-data/tree/master/datasets/AAPD/; RCV1: http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/lyrl2004_rcv1v2_README.htm; EUR-Lex: http://nlp.cs.aueb.gr/software.html].

Notes

  1. http://www.ai.mit.edu/projects/jmlr/papers/volume5/lewis04a/lyrl2004_rcv1v2_README.htm

  2. https://git.uwaterloo.ca/jimmylin/Castor-data/tree/master/datasets/AAPD/

  3. http://nlp.cs.aueb.gr/software.html

References

  1. Huang B, Guo R, Zhu Y, Fang Z, Zeng G, Liu J, Wang Y, Fujita H, Shi Z (2022) Aspect-level sentiment analysis with aspect-specific context position information. Knowl-Based Syst 243:108473. https://doi.org/10.1016/j.knosys.2022.108473

    Article  Google Scholar 

  2. Tang P, Jiang M, Xia BN, Pitera JW, Welser J, Chawla NV (2020) Multi-label patent categorization with non-local attention-based graph convolutional network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, pp 9024–9031. https://ojs.aaai.org/index.php/AAAI/article/view/6435

  3. Liu W, Wang H, Shen X, Tsang IW (2022) The emerging trends of multi-label learning. IEEE Trans Pattern Anal Mach Intell 44(11):7955–7974. https://doi.org/10.1109/TPAMI.2021.3119334

    Article  Google Scholar 

  4. Liu J, Chang W-C, Wu Y, Yang Y (2017) Deep learning for extreme multi-label text classification. In: Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval. SIGIR ’17, pp 115–124. Association for computing machinery. https://doi.org/10.1145/3077136.3080834

  5. Wu H, Qin S, Nie R, Cao J, Gorbachev S (2021) Effective collaborative representation learning for multilabel text categorization. IEEE Trans Neural Netw Learn Syst 33(10):5200–5214

    Article  Google Scholar 

  6. Huang X, Chen B, Xiao L, Yu J, Jing L (2022) Label-aware document representation via hybrid attention for extreme multi-label text classification. Neural Process Lett 54(5):3601–3617

    Article  Google Scholar 

  7. Xiao L, Zhang X, Jing L, Huang C, Song M (2021) Does head label help for long-tailed multi-label text classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp 14103–14111

  8. Zong D, Sun S (2023) Bgnn-xml: bilateral graph neural networks for extreme multi-label text classification. IEEE Trans Knowl Data Eng 35(7):6698–6709

    Google Scholar 

  9. Zhang Q-W, Zhang X, Yan Z, Liu R, Cao Y, Zhang M-L (2021) Correlation-guided representation for multi-label text classification. In: IJCAI, pp 3363–3369

  10. Ionescu RT, Butnaru A (2019) Vector of locally-aggregated word embeddings (vlawe): a novel document-level representation. In: Proceedings of the 2019 conference of the north american chapter of the association for computational linguistics: human language technologies, vol 1 (Long and Short Papers), pp 363–369. https://doi.org/10.18653/v1/N19-1033. https://aclanthology.org/N19-1033

  11. Liu M, Liu L, Cao J, Du Q (2022) Co-attention network with label embedding for text classification. Neurocomputing 471:61–69

    Article  Google Scholar 

  12. Wang J, Chen Z, Qin Y, He D, Lin F (2023) Multi-aspect co-attentional collaborative filtering for extreme multi-label text classification. Knowl-Based Syst 260:110110. https://doi.org/10.1016/j.knosys.2022.110110

    Article  Google Scholar 

  13. Chen Z-M, Wei X-S, Wang P, Guo Y (2019) Multi-label image recognition with graph convolutional networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 5177–5186

  14. Pal A, Selvakumar M, Sankarasubbu M (2020) Magnet: multi-label text classification using attention-based graph neural network. In: Proceedings of the 12th international conference on agents and artificial intelligence 1, vol 2, pp 494–505. https://doi.org/10.5220/0008940304940505

  15. Vu H, Nguyen M, Nguyen V, Tien M, Nguyen V (2022) Label correlation based graph convolutional network for multi-label text classification. In: 2022 International joint conference on neural networks (IJCNN), pp 01–08. https://ieeexplore.ieee.org/abstract/document/9892542

  16. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: International conference on learning representations (ICLR)

  17. Liang Z, Guo J, Qiu W, Huang Z, Li S (2024) When graph convolution meets double attention: online privacy disclosure detection with multi-label text classification. Data Min Knowl Discov 1–22

  18. Lewis DD, Yang Y, Russell-Rose T, Li F (2004) Rcv1: a new benchmark collection for text categorization research. J Mach Learn Res 5:361–397

    Google Scholar 

  19. Yang P, Sun X, Li W, Ma S, Wu W, Wang H (2018) Sgm: sequence generation model for multi-label classification. In: Proceedings of the 27th international conference on computational linguistics, pp 3915–3926. https://aclanthology.org/C18-1330

  20. Yang P, Luo F, Ma S, Lin J, Sun X (2019) 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. https://aclanthology.org/P19-1518

  21. Liao W, Wang Y, Yin Y, Zhang X, Ma P (2020) Improved sequence generation model for multi-label classification via cnn and initialized fully connection. Neurocomputing 382:188–195

    Article  Google Scholar 

  22. Zhang X, Tan X, Luo Z, Zhao J (2023) Multi-label sequence generating model via label semantic attention mechanism. Int J Mach Learn Cybern 14(5):1711–1723

    Article  Google Scholar 

  23. Wang R, Ridley R, Qu W, Dai X et al (2021) A novel reasoning mechanism for multi-label text classification. Inf Process Manage 58(2):102441

    Article  Google Scholar 

  24. You R, Zhang Z, Wang Z, Dai S, Mamitsuka H, Zhu S (2019) Attentionxml: label tree-based attention-aware deep model for high-performance extreme multi-label text classification. In: Advances in neural information processing systems, vol 32, pp 5820–5830

  25. Xiao L, Huang X, Chen B, Jing L (2019) 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. Association for Computational Linguistics. https://aclanthology.org/D19-1044

  26. Liu Q, Chen J, Chen F, Fang K, An P, Zhang Y, Du S (2023) Mlgn: a multi-label guided network for improving text classification. IEEE Access 11:80392–80402. https://doi.org/10.1109/ACCESS.2023.3299566

    Article  Google Scholar 

  27. Qin S, Wu H, Zhou L, Li J, Du G (2023) Learning metric space with distillation for large-scale multi-label text classification. Neural Comput Appl 35(15):11445–11458

    Article  Google Scholar 

  28. Wang Q, Zhu J, Shu H, Asamoah KO, Shi J, Zhou C (2023) Gudn: a novel guide network with label reinforcement strategy for extreme multi-label text classification. J King Saud Univ Comput Inf Sci 35(4):161–171

    Google Scholar 

  29. Xu P, Xiao L, Liu B, Lu S, Jing L, Yu J (2023) Label-specific feature augmentation for long-tailed multi-label text classification. In: Proceedings of the AAAI conference on artificial intelligence, vol. 37, pp 10602–10610

  30. Xiao L, Xu P, Song M, Liu H, Jing L, Zhang X (2023) Triple alliance prototype orthotist network for long-tailed multi-label text classification. IEEE/ACM Trans Audio Speech Lang Process 31:2616–2628. https://doi.org/10.1109/TASLP.2023.3265860

    Article  Google Scholar 

  31. Zhang W, Yan J, Wang X, Zha H (2018) Deep extreme multi-label learning. In: Proceedings of the 2018 ACM on international conference on multimedia retrieval, pp 100–107. https://doi.org/10.1145/3206025.3206030

  32. Li I, Feng A, Wu H, Li T, Suzumura T, Dong R (2022) LiGCN: label-interpretable graph convolutional networks for multi-label text classification. In: Proceedings of the 2nd workshop on deep learning on graphs for natural language processing (DLG4NLP 2022), pp 60–70. Association for Computational Linguistics. https://aclanthology.org/2022.dlg4nlp-1.7

  33. Vu H, Nguyen M, Nguyen V, Pham M, Nguyen V, Nguyen V (2023) Label-representative graph convolutional network for multi-label text classification. Appl Intell 53(12):14759–14774. https://doi.org/10.1007/s10489-022-04106-x

    Article  Google Scholar 

  34. Ma Q, Yuan C, Zhou W, Hu S (2021) 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 (vol 1: Long Papers), pp 3855–3864. Association for computational linguistics

  35. Fan C, Chen W, Tian J, Li Y, He H, Jin Y (2023) Accurate use of label dependency in multi-label text classification through the lens of causality. Appl Intell 1–17

  36. Zeng D, Zha E, Kuang J, Shen Y (2024) Multi-label text classification based on semantic-sensitive graph convolutional network. Knowl-Based Syst 284:111303

    Article  Google Scholar 

  37. Zhao F, Ai Q, Li X, Wang W, Gao Q, Liu Y (2024) Tlc-xml: transformer with label correlation for extreme multi-label text classification. Neural Process Lett 56(1):25

    Article  Google Scholar 

  38. Huang Y, Giledereli B, Köksal A, Özgür A, Ozkirimli E (2021) Balancing methods for multi-label text classification with long-tailed class distribution. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 8153–8161. Association for computational linguistics

  39. Guo H, Li X, Zhang L, Liu J, Chen W (2021) Label-aware text representation for multi-label text classification. In: ICASSP 2021-2021 IEEE International conference on acoustics, speech and signal processing (ICASSP), pp 7728–7732. https://doi.org/10.1109/ICASSP39728.2021.9413921

  40. Zhuang C, Ma Q (2018) Dual graph convolutional networks for graph-based semi-supervised classification. In: Proceedings international world wide web conferences steering committee, pp 499–508. https://doi.org/10.1145/3178876.3186116

  41. Loza Mencía E, Fürnkranz J (2008) Efficient pairwise multilabel classification for large-scale problems in the legal domain. In: Joint European conference on machine learning and knowledge discovery in databases, pp 50–65

  42. Pennington J, Socher R, Manning CD (2014) Glove: global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 1532–1543

  43. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26

  44. Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized bert pretraining approach. arXiv:1907.11692

  45. Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11)

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (No. 61862058), Natural Science Foundation of Gansu Province (No. 20JR5RA518, 21JR7RA114). Industrial Support Project of Gansu Colleges (No. 2022CYZC11).

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  1. Ben You contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

    Xiaohong Li, Ben You, Qixuan Peng & Shaojie Feng

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Contributions

X.L and B.Y: Conceptualization, Methodology, Formal analysis, Software, Investigation, Validation, Resources, Writing—original draft, review and editing, Visualization. Q.P and S.F: Resources, Writing—review and editing, Supervision.

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Correspondence to Xiaohong Li.

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Li, X., You, B., Peng, Q. et al. Dual-view graph convolutional network for multi-label text classification. Appl Intell 54, 9363–9380 (2024). https://doi.org/10.1007/s10489-024-05666-w

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