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GACaps-HTC: graph attention capsule network for hierarchical text classification

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Abstract

Hierarchical text classification has been receiving increasing attention due to its vast range of applications in real-world natural language processing tasks. While previous approaches have focused on effectively exploiting the label hierarchy for classification or capturing latent label relationships, few studies have integrated these concepts. In this work, we propose a graph attention capsule network for hierarchical text classification (GACaps-HTC), designed to capture both the explicit hierarchy and implicit relationships of labels. A graph attention network is employed to incorporate the information on the label hierarchy into a textual representation, whereas a capsule network infers classification probabilities by understanding the latent label relationships via iterative updates. The proposed approach is optimized using a loss term designed to address the innate label imbalance issue of the task. Experiments were conducted on two widely used text classification datasets, the WOS-46985 dataset and the RCV1 dataset. The results reveal that the proposed approach achieved a 0.6% gain and a 2.0% gain in micro-F1 and macro-F1 scores, respectively, on the WOS-46985 dataset and a 0.3% gain and a 2.2% gain in micro-F1 and macro-F1 scores, respectively, on the RCV1 dataset compared to the previous state-of-the-art approaches. Further ablation studies show that each component in GACaps-HTC played a part in enhancing the classification performance.

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Data Availability

The datasets analysed using the current study are available in the Mendeley Data repository (https://data.mendeley.com/datasets/9rw3vkcfy4/6) and the Text Retrieval Conference repository (https://trec.nist.gov/data/reuters/reuters.html) as described in the manuscript.

Code Availability

The code for reproducing the results provided in the manuscript will be made public upon acceptance.

Notes

  1. https://www.webofscience.com/

  2. https://data.mendeley.com/datasets/9rw3vkcfy4/6

  3. https://www.reuters.com/

  4. https://trec.nist.gov/data/reuters/reuters.html

References

  1. Liu X, Gao J, He X et al (2015) Representation learning using multi-task deep neural networks for semantic classification and information retrieval. In: Proc Conf North American Chapter Assoc Comput Linguist: Human lang Technol. ACL, pp 912-921. https://doi.org/10.3115/v1/n15-1092

  2. Panda SP, Mohanty JP (2020) A domain classification-based information retrieval system. In: Proc IEEE int Women eng Conf Electr Comput Eng. IEEE, pp 122-125. https://doi.org/10.1109/WIECON-ECE52138.2020.9398018

  3. Wu Z, Gao J, Li Q et al (2022) Make aspect-based sentiment classification go further: step into the long-document-level. Appl Intell 52(8):8428–8447. https://doi.org/10.1007/s10489-021-02836-y

    Article  Google Scholar 

  4. Liao W, Zeng B, Yin X et al (2021) An improved aspect-category sentiment analysis model for text sentiment analysis based on roberta. Appl Intell 51(6):3522–3533. https://doi.org/10.1007/s10489-020-01964-1

    Article  Google Scholar 

  5. Do P, Phan T H (2022) Developing a bert based triple classification model using knowledge graph embedding for question answering system. Appl Intell 52(1):636–651. https://doi.org/10.1007/s10489-021-02460-w

    Article  Google Scholar 

  6. Yan M, Pan Y (2022) Meta-learning for compressed language model: a multiple choice question answering study. Neurocomputing 487:181–189. https://doi.org/10.1016/j.neucom.2021.01.148

    Article  Google Scholar 

  7. Lewis D D, Yang Y, Russell-Rose T et al (2004) Rcv1: a new benchmark collection for text categorization research. J Mach Learn Res 5(Apr):361–397. https://doi.org/10.5555/1005332.1005345

    Google Scholar 

  8. Abdelgawad L, Kluegl P, Genc E et al (2019) Optimizing neural networks for patent classification. In: Proc jt European conf Mach Learn Knowl Discov Databases. Springer, pp 688-703. https://doi.org/10.1007/978-3-030-46133-1_41

  9. Kowsari K, Brown D E, Heidarysafa M et al (2017) Hdltex: Hierarchical deep learning for text classification. In: Proc IEEE int Conf Mach Learn Appl. IEEE, pp 364-371. https://doi.org/10.1109/ICMLA.2017.0-134

  10. Yu W, Sun Z, Liu H et al (2018) Multi-level deep learning based e-commerce product categorization. In: Proc Int ACM SIGIR conf Res Develop Inf Retr Workshop E-Commer. ACM, pp 1-6

  11. Perez A R, Martinez L M, Delfino J M (2017) Physicochemical stability and rheologic properties of a natural hydrating and exfoliating formulation beneficial for the treatment of skin xeroses. Latin American J Pharm 36:157–164

    Google Scholar 

  12. Zhang X, Zhang Q W, Yan Z et al (2021a) Enhancing label correlation feedback in multi-label text classification via multi-task learning. In: Proc Annu Meet Assoc Comput Linguist. ACL, pp 1190-1200. https://doi.org/10.18653/v1/2021.findings-acl.101

  13. Zhang QW, Zhang X, Yan Z et al (2021b) Correlation-guided representation for multi-label text classification. In: Proc Int Jt Conf Artif Intell, pp 3363–3369. https://doi.org/10.24963/ijcai.2021/463

  14. Chen B, Huang X, Xiao L et al (2020) Hyperbolic interaction model for hierarchical multi-label classification. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 7496–7503

  15. Mao Y, Tian J, Han J et al (2019) Hierarchical text classification with reinforced label assignment. In: Proc Conf Empir Methods nat Lang Process. ACL, pp 445-455. https://doi.org/10.18653/v1/D19-1042

  16. Lu J, Du L, Liu M et al (2020) Multi-label few/zero-shot learning with knowledge aggregated from multiple label graphs. In: Proc Conf Empir Methods nat Lang Process. ACL, pp 2935-2943. https://doi.org/10.18653/v1/2020.emnlp-main.235

  17. Zhou J, Ma C, Long D et al (2020) Hierarchy-aware global model for hierarchical text classification. in: Proc. Annu. Meet. Assoc. Comput. ACL, Linguist., pp 1106–1117. https://doi.org/10.18653/v1/2020.acl-main.104

  18. Deng Z, Peng H, He D et al (2021) Htcinfomax: a global model for hierarchical text classification via information maximization. In: Proc Conf North American Chapter Assoc Comput Linguist: Human lang Technol. ACL, pp 3259-3265. https://doi.org/10.18653/v1/2021.naacl-main.260

  19. Gopal S, Yang Y (2013) Recursive regularization for large-scale classification with hierarchical and graphical dependencies. In: Proc ACM SIGKDD int Conf Knowl Discov Data Min. ACM, pp 257-265. https://doi.org/10.1145/2487575.2487644

  20. Peng H, Li J, He Y et al (2018) Large-scale hierarchical text classification with recursively regularized deep graph-cnn. in: Proc, World Wide Web Conf., ACM. https://doi.org/10.1145/3178876.3186005

  21. Yu Y, Sun Z, Sun C et al (2021) Hierarchical multilabel text classification via multitask learning. In: Proc IEEE int Conf Tools artif Intell. IEEE, pp 1138-1143. https://doi.org/10.1109/ICTAI52525.2021.00180

  22. Wang R, Long S, Dai X et al (2021) Meta-lmtc: meta-learning for large-scale multi-label text classification. In: Proc Conf Empir Methods nat Lang Process. ACL, pp 8633-8646. https://doi.org/10.18653/v1/2021.emnlp-main.679

  23. Chatterjee S, Maheshwari A, Ramakrishnan G et al (2021) Joint learning of hyperbolic label embeddings for hierarchical multi-label classification. In: Proc Conf European assoc Comput Linguist. ACL, pp 2829-2841. https://doi.org/10.48550/arXiv.2101.04997

  24. Chai D, Wu W, Han Q et al (2020) Description based text classification with reinforcement learning. In: Proceedings of the international conference on machine learning. PMLR, pp 1371–1382

  25. Hang JY, Zhang ML (2021) Collaborative learning of label semantics and deep label-specific features for multi-label classification. IEEE Trans Patt Anal Mach Intell. https://doi.org/10.1109/TPAMI.2021.3136592

  26. Wang Z, Wang P, Huang L et al (2022) Incorporating hierarchy into text encoder: a contrastive learning approach for hierarchical text classification. In: Proc Annu Meet Assoc Comput Linguist. ACL, pp 7109-7119. https://doi.org/10.48550/arXiv.2203.03825

  27. Veličković P, Cucurull G, Casanova A et al (2018) Graph attention networks. In: Proc Int Conf Learn Represent, pp 1–12. https://doi.org/10.48550/arXiv.1710.10903. https://openreview.net/forum?id=rJXMpikCZ

  28. Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Proc Int Conf Learn Represent, pp 1–15. https://doi.org/10.48550/arXiv.1409.0473

  29. Hinton G E, Krizhevsky A, Wang SD (2011) Transforming auto-encoders. In: Int Conf Artif Neural Netw. Springer, pp 44-51. https://doi.org/10.1007/978-3-642-21735-7_6

  30. Aly R, Remus S, Biemann C (2019) Hierarchical multi-label classification of text with capsule networks

  31. Peng H, Li J, Wang S et al (2019) Hierarchical taxonomy-aware and attentional graph capsule rcnns for large-scale multi-label text classification. IEEE Trans Knowl Data Eng 33(6):2505–2519. https://doi.org/10.1109/TKDE.2019.2959991

    Article  Google Scholar 

  32. Sabour S, Frosst N, Hinton GE (2017) Dynamic routing between capsules. In: Proc Adv Neural Inf Process Syst, pp 3859–3869. https://doi.org/10.5555/3294996.3295142

  33. Zhao W, Peng H, Eger S et al (2019) Towards scalable and reliable capsule networks for challenging nlp applications. In: Proc Annu Meet Assoc Comput Linguist. ACL, pp 1549-1559. https://doi.org/10.18653/v1/P19-1150

  34. Huang W, Zhou F (2020) Da-capsnet: dual attention mechanism capsule network. Sci Rep 10(1):1–13. https://doi.org/10.1038/s41598-020-68453-w

    MathSciNet  Google Scholar 

  35. Xiang C, Zhang L, Tang Y et al (2018) Ms-capsnet: a novel multi-scale capsule network. IEEE Signal Process Lett 25(12):1850–1854. https://doi.org/10.1109/LSP.2018.2873892

    Article  Google Scholar 

  36. Jeong T, Lee Y, Kim H (2019) Ladder capsule network. In: Proc Int Conf Mach Learn. PMLR, pp 3071-3079

  37. Silla C N, Freitas A A (2011) A survey of hierarchical classification across different application domains. Data Min Knowl Discov 22(1):31–72. https://doi.org/10.1007/s10618-010-0175-9

    Article  MathSciNet  MATH  Google Scholar 

  38. Fürnkranz J, Hüllermeier E, Loza Mencía E et al (2008) Multilabel classification via calibrated label ranking. Mach Learn 73(2):133–153. https://doi.org/10.1007/s10994-008-5064-8

    Article  MATH  Google Scholar 

  39. Johnson R, Zhang T (2015) Effective use of word order for text categorization with convolutional neural networks. In: Proc Conf North American Chapter Assoc Comput Linguist: Human lang Technol. ACL, pp 103-112. https://doi.org/10.3115/v1/N15-1011

  40. Dumais S, Chen H (2000) Hierarchical classification of web content. In: Proc Int ACM SIGIR conf Res Develop Inf Retr. ACM, pp 256-263. https://doi.org/10.1145/345508.345593

  41. Moyano J M, Gibaja E L, Cios K J et al (2018) Review of ensembles of multi-label classifiers: models, experimental study and prospects. Inf Fusion 44:33–45. https://doi.org/10.1016/j.inffus.2017.12.001

    Article  Google Scholar 

  42. Fagni T, Sebastiani F (2010) Selecting negative examples for hierarchical text classification: an experimental comparison. J American Soc Inf Sci Technol 61(11):2256–2265. https://doi.org/10.5555/1869064.1869084

    Article  Google Scholar 

  43. Banerjee S, Akkaya C, Perez-Sorrosal F et al (2019) Hierarchical transfer learning for multi-label text classification. In: Proc Annu Meet Assoc Comput Linguist. ACL, pp 6295-6300. https://doi.org/10.18653/v1/P19-1633

  44. Krendzelak M, Jakab F (2019) Hierarchical text classification using cnns with local classification per parent node approach. In: Int Conf Emerg elearning technol Appl. IEEE, pp 460-464. https://doi.org/10.1109/ICETA48886.2019.9040022

  45. Shimura K, Li J, Fukumoto F (2018) Hft-cnn: learning hierarchical category structure for multi-label short text categorization. In: Proc Conf Empir Methods nat Lang Process. ACL, pp 811-816. https://doi.org/10.18653/v1/D18-1093

  46. Wehrmann J, Cerri R, Barros R (2018) Hierarchical multi-label classification networks. In: Proc Int Conf Mach Learn. PMLR, pp 5075–5084

  47. Huang W, Chen E, Liu Q et al (2019) Hierarchical multi-label text classification: an attention-based recurrent network approach. In: Proc ACM int Conf Info Knowl Manag. ACM, pp 1051-1060. https://doi.org/10.1145/3357384.3357885

  48. Zhang X, Xu J, Soh C et al (2022) La-hcn: Label-based attention for hierarchical multi-label text classification neural network. Expert Syst Appl 187:115–922. https://doi.org/10.1016/j.eswa.2021.115922

    Article  Google Scholar 

  49. Scarselli F, Gori M, Tsoi A C et al (2008) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80. https://doi.org/10.1109/TNN.2008.2005605

    Article  Google Scholar 

  50. Chen H, Ma Q, Lin Z et al (2021) Hierarchy-aware label semantics matching network for hierarchical text classification. In: Proc Annu Meet Assoc Comput Linguist. ACL, pp 4370-4379. https://doi.org/10.18653/v1/2021.acl-long.337

  51. Xu L, Teng S, Zhao R et al (2021) Hierarchical multi-label text classification with horizontal and vertical category correlations. In: Proc Conf Empir Methods nat Lang Process. ACL, pp 2459-2468. https://doi.org/10.18653/v1/2021.emnlp-main.190

  52. Wu J, Xiong W, Wang W Y (2019) Learning to learn and predict: a meta-learning approach for multi-label classification. In: Proc Conf Empir Methods nat. lang process. ACL, pp 4354-4364. https://doi.org/10.18653/v1/D19-1444

  53. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. In: Proc int conf learn represent, pp 1–14. https://doi.org/10.48550/arXiv.1609.02907. https://openreview.net/forum?id=SJU4ayYgl

  54. Yang F, Zhang H, Tao S (2022) Semi-supervised classification via full-graph attention neural networks. Neurocomputing 476:63–74. https://doi.org/10.1016/j.neucom.2021.12.077

    Article  Google Scholar 

  55. Jo J, Baek J, Lee S et al (2021) Edge representation learning with hypergraphs. In: Proc Adv Neural Inf Process Syst, pp 1–13. https://doi.org/10.48550/arXiv.2106.15845. https://openreview.net/forum?id=vwgsqRorzz

  56. Luo J, Li C, Fan Q et al (2022) A graph convolutional encoder and multi-head attention decoder network for tsp via reinforcement learning. Eng Appl Artif Intell 112:104–848. https://doi.org/10.1016/j.engappai.2022.104848

    Article  Google Scholar 

  57. Ying Z, You J, Morris C et al (2018) Hierarchical graph representation learning with differentiable pooling. In: Proc Adv Neural Inf Process Syst, pp 4805–4815. https://doi.org/10.48550/arXiv.1806.08804

  58. Ma Y, Wang S, Aggarwal CC et al (2019) Graph convolutional networks with eigenpooling. In: Proc ACM SIGKDD int Conf Knowl Discov Data Min. ACM, pp 723-731. https://doi.org/10.1145/3292500.3330982

  59. Gallicchio C, Micheli A (2010) Graph echo state networks. In: Int jt Conf Neural Netw. IEEE, pp 1-8. https://doi.org/10.1109/IJCNN.2010.5596796

  60. Gallicchio C, Micheli A (2013) Tree echo state networks. Neurocomputing 101:319–337. https://doi.org/10.1016/j.neucom.2012.08.017

    Article  Google Scholar 

  61. Bruna J, Zaremba W, Szlam A et al (2014) Spectral networks and deep locally connected networks on graphs. In: Proc Int Conf Learn Represent, pp 1–14. https://doi.org/10.48550/arXiv.1312.6203. https://openreview.net/forum?id=DQNsQf-UsoDBa

  62. Srivastava N, Hinton G, Krizhevsky A et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(56):1929–1958. https://doi.org/10.5555/2627435.2670313

    MathSciNet  MATH  Google Scholar 

  63. Gu J, Tresp V (2020) Improving the robustness of capsule networks to image affine transformations. In: Proc IEEE/CVF conf Comput Vis Pattern Recognit. IEEE, pp 7285-7293. https://doi.org/10.1109/CVPR42600.2020.00731

  64. Zhao W, Ye J, Yang M et al (2018) Investigating capsule networks with dynamic routing for text classification. In: Proc Conf Empir Methods nat Lang Process. ACL, pp 3110-3119. https://doi.org/10.18653/v1/D18-1350

  65. Cheng Y, Zou H, Sun H et al (2022) Hsan-capsule: a novel text classification model. Neurocomputing 489:521–533. https://doi.org/10.1016/j.neucom.2021.12.064

    Article  Google Scholar 

  66. Lai S, Xu L, Liu K et al (2015) Recurrent convolutional neural networks for text classification. In: Proc AAAI Conf Artif Intell, pp 2267–2273. https://doi.org/10.5555/2886521.2886636

  67. Vaswani A, Shazeer N, Parmar N et al (2017) Attention is all you need. In: Proc Adv Neural Inf Process Syst, pp 6000–6010. https://doi.org/10.5555/3295222.3295349

  68. Nair V, Hinton GE (2010) Rectified linear units improve restricted boltzmann machines. In: Proc Int Conf Learn Represent, pp 1–8. https://doi.org/10.5555/3104322.3104425. https://openreview.net/forum?id=rkb15iZdZB

  69. Maas AL, Hannun AY, Ng AY (2013) Rectifier nonlinearities improve neural network acoustic models. In: Proc Int Conf Mach Learn Workshop Deep Learn Audio Speech Lang Process, pp 1–6. https://doi.org/10.1.1.693.1422

  70. Pereira RM, Costa YM, Silla CN (2021) Handling imbalance in hierarchical classification problems using local classifiers approaches. Data Min Knowl Discov 35(4):1564–1621. https://doi.org/10.1007/s10618-021-00762-8

    Article  MathSciNet  MATH  Google Scholar 

  71. Lin T Y, Goyal P, Girshick R et al (2017) Focal loss for dense object detection. In: Proc IEEE int Conf Comput Vis. IEEE, pp 2980-2988. https://doi.org/10.1109/ICCV.2017.324

  72. Shah A, Sra S, Chellappa R et al (2022) Max-margin contrastive learning. In: Proc AAAI Conf Artif Intell, pp 8220–8230. https://doi.org/10.1609/aaai.v36i8.20796

  73. Chen H, Sun M, Tu C et al (2016) Neural sentiment classification with user and product attention. In: Proc Conf Empir Methods nat Lang Process. ACL, pp 1650-1659. https://doi.org/10.18653/v1/D16-1171

  74. Hochreiter S, Schmidhuber J (1997) Long Short-Term memory. Neural Comput 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  75. Yang Z, Yang D, Dyer C et al (2016) Hierarchical attention networks for document classification. In: Proc conf north American chapter assoc comput linguist: human lang technol. ACL, pp 1480-1489. https://doi.org/10.18653/v1/N16-1174

  76. Zhou P, Shi W, Tian J et al (2016) Attention-based bidirectional long short-term memory networks for relation classification. In: Proc Annu Meet Assoc Comput Linguist. ACL, pp 207-212. https://doi.org/10.18653/v1/P16-2034

  77. Liu J, Chang WC, Wu Y et al (2017) Deep learning for extreme multi-label text classification. In: Proc Int ACM SIGIR conf Res Develop Inf Retr. ACM, pp 115-124. https://doi.org/10.1145/3077136.3080834

  78. Mou L, Meng Z, Yan R et al (2016) How transferable are neural networks in NLP applications?. In: Proc Conf Empir Methods Nat Lang Process, pp 479–489. https://doi.org/10.18653/v1/D16-1046

  79. Cho K, van Merriënboer B, Bahdanau D et al (2014) On the properties of neural machine translation: encode–decoder approaches. In: Proc Workshop Syntax Semant Struct Statistical Translation, pp 103–111. https://doi.org/10.3115/v1/W14-4012

  80. Devlin J, Chang M W, Lee K et al (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proc conf north American chapter assoc comput linguist: human lang technol. ACL, pp 4171-4186. https://doi.org/10.18653/v1/N19-1423

  81. Nguyen M T, Le D T, Le L (2021) Transformers-based information extraction with limited data for domain-specific business documents. Eng Appl Artif Intell 97:104–100. https://doi.org/10.1016/j.engappai.2020.104100

  82. Uymaz HA, Metin SK (2022) Vector based sentiment and emotion analysis from text: a survey. Eng Appl Artif Intell 113:104–922. https://doi.org/10.1016/j.engappai.2022.104922

    Google Scholar 

  83. Beltagy I, Lo K, Cohan A (2019) Scibert: a Pretrained language model for scientific text. In: Proc Conf Empir Methods nat Lang Process. ACL, pp 3615-3620. https://doi.org/10.18653/v1/D19-1371

  84. Kingma DP, Ba J (2015) Adam: a method for stochastic optimization. In: Proc Int Conf Learn Represent, pp 1–15. https://doi.org/10.48550/arXiv.1412.6980. https://openreview.net/forum?id=8gmWwjFyLj

  85. Wang B, Hu X, Li P et al (2021) Cognitive structure learning model for hierarchical multi-label text classification. Knowl-Based Syst 218:106–876. https://doi.org/10.1016/j.knosys.2021.106876

    Article  Google Scholar 

  86. Abuselidze G (2019) Modern challenges of monetary policy strategies: inflation and devaluation influence on economic development of the country. Acad Strateg Manag J 18(4):1–10

    Google Scholar 

  87. Park J, Cho J, Chang H J et al (2021) Unsupervised hyperbolic representation learning via message passing auto-encoders. In: Proc IEEE/CVF conf Comput Vis Pattern Recognit. IEEE, pp 5516-5526. https://doi.org/10.1109/CVPR46437.2021.00547

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1F1A1053366). The Institute of Engineering Research at Seoul National University provided research facilities for this work.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1F1A1053366). The Institute of Engineering Research at Seoul National University provided research facilities for this work.

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All authors contributed to the conceptualization. Methodology design, analysis, and visualization were performed by Jinhyun Bang. Funding was acquired by Jonghun Park. This study was supervised by Jonghun Park and Jonghyuk Park. Original draft of the manuscript was written by Jinhyun Bang and reviewed, edited, and approved by all authors.

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Correspondence to Jonghyuk Park.

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Bang, J., Park, J. & Park, J. GACaps-HTC: graph attention capsule network for hierarchical text classification. Appl Intell 53, 20577–20594 (2023). https://doi.org/10.1007/s10489-023-04585-6

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