Skip to main content
SpringerLink
Log in

BART-based contrastive and retrospective network for aspect-category-opinion-sentiment quadruple extraction

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Aspect-category-opinion-sentiment (ACOS) quadruple extraction is a fine-grained sentiment analysis task to extract full sentiment information, which aims to extract all the ACOS quads in a given sentence. ACOS contains four types of quads: explicit aspect and explicit opinion, implicit aspect and explicit opinion, explicit aspect and implicit opinion, and implicit aspect and implicit opinion. Current studies generally apply the two-stage methods to ACOS studies. However, there are two main limitations. One is the error propagation while the other is the ignorance of diversity among different types of quads. In this work, we propose a BART-based Contrastive and Retrospective Network (BART-CRN), which tackles ACOS extraction as a sequence generation task. Specifically, a machine reading comprehension based (MRC-based) supervised contrastive and retrospective learning module is developed, which aims to learn the associations among all types of quads and determines the context-related generative quads through an end-to-end way. Experimental results on two ACOS datasets reveal that our model outperforms the baseline methods and achieves advanced performances.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. The code is available on https://github.com/xiaodou12046/BART_CRN.

References

  1. Hu M, Liu B( 2004) Mining and summarizing customer reviews. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’04, pp. 168– 177. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/1014052.1014073

  2. Li K, Chen C, Quan X, Ling Q, Song Y (2020) Conditional augmentation for aspect term extraction via masked sequence-to-sequence generation. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7056– 7066. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2020.acl-main.631. https://aclanthology.org/2020.acl-main.631

  3. Wang W, Pan SJ, Dahlmeier D, Xiao X (2016) Recursive neural conditional random fields for aspect-based sentiment analysis. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 616– 626. Association for Computational Linguistics, Austin, Texas. https://doi.org/10.18653/v1/D16-1059. https://aclanthology.org/D16-1059

  4. He R, Lee WS, Ng HT, Dahlmeier D (2019) An interactive multi-task learning network for end-to-end aspect-based sentiment analysis. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 504– 515. Association for Computational Linguistics, Florence, Italy. https://doi.org/10.18653/v1/P19-1048. https://aclanthology.org/P19-1048

  5. Tang D, Qin B, Feng X, Liu T(2016) Effective LSTMs for target-dependent sentiment classification. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 3298– 3307. The COLING 2016 Organizing Committee, Osaka, Japan. https://aclanthology.org/C16-1311

  6. Xue W, Li T(2018) Aspect based sentiment analysis with gated convolutional networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2514– 2523. Association for Computational Linguistics, Melbourne, Australia. https://doi.org/10.18653/v1/P18-1234. https://aclanthology.org/P18-1234

  7. Sun K, Zhang R, Mensah S, Mao Y, Liu X(2019) Aspect-level sentiment analysis via convolution over dependency tree. 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. 5679– 5688. Association for Computational Linguistics, Hong Kong, China. https://doi.org/10.18653/v1/D19-1569. https://aclanthology.org/D19-1569

  8. Tang H, Ji D, Li C, Zhou Q(2020) Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6578– 6588. Association for Computational Linguistics, Online https://doi.org/10.18653/v1/2020.acl-main.588. https://aclanthology.org/2020.acl-main.588

  9. Zhang W, Li X, Deng Y, Bing L, Lam W (2022) A survey on aspect-based sentiment analysis: Tasks, methods, and challenges. arXiv preprint arXiv:2203.01054

  10. Peng H, Xu L, Bing L, Huang F, Lu W, Si L( 2020) Knowing what, how and why: A near complete solution for aspect-based sentiment analysis. Proceedings of the AAAI Conference on Artificial Intelligence 34( 05), 8600– 8607. https://doi.org/10.1609/aaai.v34i05.6383

  11. Xu L, Li H, Lu W, Bing L ( 2020) Position-aware tagging for aspect sentiment triplet extraction. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 2339– 2349. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2020.emnlp-main.183. https://aclanthology.org/2020.emnlp-main.183

  12. Xu L, Chia YK, Bing L ( 2021) Learning span-level interactions for aspect sentiment triplet extraction. 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. 4755– 4766. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2021.acl-long.367. https://aclanthology.org/2021.acl-long.367

  13. Wu Z, Ying C, Zhao F, Fan Z, Dai X, Xia R ( 2020) Grid tagging scheme for aspect-oriented fine-grained opinion extraction. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 2576– 2585. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2020.findings-emnlp.234. https://aclanthology.org/2020.findings-emnlp.234

  14. Chen Z, Huang H, Liu B, Shi X, Jin H ( 2021) Semantic and syntactic enhanced aspect sentiment triplet extraction. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 1474– 1483. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2021.findings-acl.128. https://aclanthology.org/2021.findings-acl.128

  15. Chen S, Wang Y, Liu J, Wang Y (2021) Bidirectional machine reading comprehension for aspect sentiment triplet extraction. Proceedings of the AAAI Conference on Artificial Intelligence 35(14):12666–12674

    Article  Google Scholar 

  16. Mao Y, Shen Y, Yu C, Cai L (2021) A joint training dual-mrc framework for aspect based sentiment analysis. Proceedings of the AAAI Conference on Artificial Intelligence 35(15):13543–13551

    Article  Google Scholar 

  17. Cai H, Xia R, Yu J ( 2021) Aspect-category-opinion-sentiment quadruple extraction with implicit aspects and opinions. 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. 340– 350. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2021.acl-long.29. https://aclanthology.org/2021.acl-long.29

  18. Zhang W, Deng Y, Li X, Yuan Y, Bing L, Lam W (2021) Aspect sentiment quad prediction as paraphrase generation. arXiv preprint arXiv:2110.00796

  19. Peper JJ, Wang L (2022) Generative aspect-based sentiment analysis with contrastive learning and expressive structure. arXiv preprint arXiv:2211.07743

  20. Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., Zettlemoyer, L ( 2020) BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871– 7880. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2020.acl-main.703. https://aclanthology.org/2020.acl-main.703

  21. Athiwaratkun B, Nogueira dos Santos C, Krone J, Xiang B( 2020) Augmented natural language for generative sequence labeling. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 375– 385. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2020.emnlp-main.27. https://aclanthology.org/2020.emnlp-main.27

  22. Yan, H., Dai, J., Ji, T., Qiu, X., Zhang, Z.: A unified generative framework for aspect-based sentiment analysis. 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. 2416– 2429. Association for Computational Linguistics, Online ( 2021). https://doi.org/10.18653/v1/2021.acl-long.188. https://aclanthology.org/2021.acl-long.188

  23. Gao, T., Yao, X., Chen, D.: SimCSE: Simple contrastive learning of sentence embeddings. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 6894– 6910. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic ( 2021). https://doi.org/10.18653/v1/2021.emnlp-main.552. https://aclanthology.org/2021.emnlp-main.552

  24. Pan, X., Wang, M., Wu, L., Li, L.: Contrastive learning for many-to-many multilingual neural machine translation. 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. 244– 258. Association for Computational Linguistics, Online ( 2021). https://doi.org/10.18653/v1/2021.acl-long.21. https://aclanthology.org/2021.acl-long.21

  25. Ziqi Wang, Xiaozhi Wang, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, and Jie Zhou. 2021. CLEVE: Contrastive Pre-training for Event Extraction. 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), pages 6283–6297, Online. Association for Computational Linguistics.

  26. Su, Y., Lan, T., Wang, Y., Yogatama, D., Kong, L., Collier, N.: A contrastive framework for neural text generation. arXiv preprint arXiv:2202.06417 (2022)

  27. Holtzman A, Buys J, Du L, Forbes M, Choi Y (2019) The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751

  28. Welleck S, Kulikov I, Roller S, Dinan E, Cho K, Weston J (2019) Neural text generation with unlikelihood training. arXiv preprint arXiv:1908.04319

  29. Ye H, Zhang N, Deng S, Chen M, Tan C, Huang F, Chen H (2021) Contrastive triple extraction with generative transformer. Proceedings of the AAAI Conference on Artificial Intelligence 35(16):14257–14265

    Article  Google Scholar 

  30. Gu Y, Qu X, Wang Z, Huai B, Yuan NJ, Gui X (2021) Read, retrospect, select: An mrc framework to short text entity linking. Proceedings of the AAAI Conference on Artificial Intelligence 35(14):12920–12928

    Article  Google Scholar 

  31. Tang D, Wei F, Yang N, Zhou M, Liu T, Qin B ( 2014) Learning sentiment-specific word embedding for Twitter sentiment classification. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1555– 1565. Association for Computational Linguistics, Baltimore, Maryland. https://doi.org/10.3115/v1/P14-1146. https://aclanthology.org/P14-1146

  32. Yang M, Tu W, Wang J, Xu F, Chen X (2017) Attention based lstm for target dependent sentiment classification. Proceedings of the AAAI Conference on Artificial Intelligence 31(1)

  33. Cambria E, Das D, Bandyopadhyay S, Feraco A (2017) Affective computing and sentiment analysis. A Practical Guide to Sentiment Analysis. Springer, New York, pp 1–10

    Chapter  Google Scholar 

  34. Pang S, Xue Y, Yan Z, Huang W, Feng J ( 2021) Dynamic and multi-channel graph convolutional networks for aspect-based sentiment analysis. In: Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 2627– 2636. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2021.findings-acl.232. https://aclanthology.org/2021.findings-acl.232

  35. Wang W, Pan SJ ( 2018) Recursive neural structural correspondence network for cross-domain aspect and opinion co-extraction. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2171– 2181. Association for Computational Linguistics, Melbourne, Australia. https://doi.org/10.18653/v1/P18-1202. https://aclanthology.org/P18-1202

  36. Yu J, Jiang J, Xia R (2019) Global inference for aspect and opinion terms co-extraction based on multi-task neural networks. IEEE/ACM Transact Audio Speech Language Proces 27(1):168–177. https://doi.org/10.1109/TASLP.2018.2875170

    Article  Google Scholar 

  37. Wu Z, Zhao F, Dai X-Y, Huang S, Chen J (2020) Latent opinions transfer network for target-oriented opinion words extraction. Proceedings of the AAAI Conference on Artificial Intelligence 34(05):9298–9305. https://doi.org/10.1609/aaai.v34i05.6469

    Article  Google Scholar 

  38. Zhao H, Huang L, Zhang R, Lu Q, Xue H ( 2020) SpanMlt: A span-based multi-task learning framework for pair-wise aspect and opinion terms extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3239– 3248. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2020.acl-main.296. https://aclanthology.org/2020.acl-main.296

  39. Ma D, Li S, Wang H ( 2018) Joint learning for targeted sentiment analysis. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pp. 4737– 4742. Association for Computational Linguistics, Brussels, Belgium. https://doi.org/10.18653/v1/D18-1504. https://aclanthology.org/D18-1504

  40. Li X, Bing L, Li P, Lam W (2019) A unified model for opinion target extraction and target sentiment prediction. Proceedings of the AAAI Conference on Artificial Intelligence 33(01):6714–6721. https://doi.org/10.1609/aaai.v33i01.33016714

    Article  Google Scholar 

  41. Chen Z, Qian T ( 2020) Relation-aware collaborative learning for unified aspect-based sentiment analysis. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 3685– 3694. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2020.acl-main.340. https://aclanthology.org/2020.acl-main.340

  42. Bao X, Wang Z, Jiang X, Xiao R, Li S (2022) Aspect-based sentiment analysis with opinion tree generation. IJCAI 2022:4044–4050

    Google Scholar 

  43. Mao Y, Shen Y, Yang J, Zhu X, Cai, L ( 2022) Seq2path: Generating sentiment tuples as paths of a tree. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 2215– 2225

  44. Liu B (2012) Sentiment Analysis and Opinion Mining. Morgan & Claypool Publishers, San Rafael

    Book  Google Scholar 

  45. Liao J, Wang S, Li D (2019) Identification of fact-implied implicit sentiment based on multi-level semantic fused representation. Knowledge-Based Systems 165:197–207. https://doi.org/10.1016/j.knosys.2018.11.023

    Article  Google Scholar 

  46. Wei J, Liao J, Yang Z, Wang S, Zhao Q (2020) Bilstm with multi-polarity orthogonal attention for implicit sentiment analysis. Neurocomputing 383:165–173. https://doi.org/10.1016/j.neucom.2019.11.054

    Article  Google Scholar 

  47. Li Z, Zou Y, Zhang C, Zhang Q, Wei Z ( 2021) Learning implicit sentiment in aspect-based sentiment analysis with supervised contrastive pre-training. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 246– 256. Association for Computational Linguistics, Online and Punta Cana, Dominican Republic. https://doi.org/10.18653/v1/2021.emnlp-main.22. https://aclanthology.org/2021.emnlp-main.22

  48. Chen T, Kornblith S, Norouzi M (2020) A simple framework for contrastive learning of visual representations. In: III, H.D., Singh, A. (eds.) Proceedings of the 37th International Conference on Machine Learning. Proceedings of Machine Learning Research, PMLR 119:1597–1607 (https://proceedings.mlr.press/v119/chen20j.html)

    Google Scholar 

  49. Chen X, Fan H, Girshick R, et al. Improved baselines with momentum contrastive learning[J]. arXiv preprint arXiv:2003.04297, 2020

  50. Khosla P, Teterwak P, Wang C et al (2020) Supervised contrastive learning[J]. Advances in neural information processing systems 33:18661–18673

    Google Scholar 

  51. Giorgi J, Nitski O, Wang B, et al. Declutr: Deep contrastive learning for unsupervised textual representations[J]. arXiv preprint arXiv:2006.03659, 2020

  52. Yan Y, Li R, Wang S, et al. Consert: A contrastive framework for self-supervised sentence representation transfer[J]. arXiv preprint arXiv:2105.11741, 2021

  53. Kim T, Yoo KM, Lee S-g ( 2021) Self-guided contrastive learning for BERT sentence representations. 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. 2528– 2540. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2021.acl-long.197. https://aclanthology.org/2021.acl-long.197

  54. Qiu G, Liu B, Bu J, Chen C (2011) Opinion word expansion and target extraction through double propagation. Comput Linguist 37(1):9–27. https://doi.org/10.1162/coli_a_00034

    Article  Google Scholar 

  55. van der Maaten L, Hinton G (2008) Visualizing Data using t-SNE[J]. Journal of Machine Learning Research 9:2579–2605

    MATH  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2023A1515011370, the Characteristic Innovation Projects of Guangdong Colleges and Universities (No. 2018KTSCX049), the Science and Technology Plan Project of Guangzhou under Grant No. 202102080258.

Author information

Authors and Affiliations

  1. School of Electronics and Information Engineering, South China Normal University, Foshan, 528225, China

    Haoliang Xiong, Yun Xue & Qianhua Cai

  2. School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, 510006, China

    Zehao Yan, Guojun Lu, Shiguan Pang & Yun Xue

  3. Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China

    Chuhan Wu

Authors
  1. Haoliang Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zehao Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chuhan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guojun Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Shiguan Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yun Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qianhua Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Zehao Yan or Qianhua Cai.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiong, H., Yan, Z., Wu, C. et al. BART-based contrastive and retrospective network for aspect-category-opinion-sentiment quadruple extraction. Int. J. Mach. Learn. & Cyber. 14, 3243–3255 (2023). https://doi.org/10.1007/s13042-023-01831-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-023-01831-8

Keywords

Search

Navigation