A mutual mean teacher framework for cross-domain aspect-based sentiment analysis
Author:
Funder
National Key Research and Development Program of China
Publisher
Springer Science and Business Media LLC
Subject
Hardware and Architecture,Information Systems,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s11227-023-05792-1.pdf
Reference61 articles.
1. Liu B (2012) Sentiment analysis and opinion mining. Synth Lect Hum Lang Technol 1:1–167. https://doi.org/10.1007/978-3-031-02145-9
2. Li X, Bing L, Li P, Lam W (2019) A unified model for opinion target extraction and target sentiment prediction. In: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence. AAAI’19/IAAI’19/EAAI’19. AAAI Press. https://doi.org/10.1609/aaai.v33i01.33016714
3. 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
4. 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. Association for Computational Linguistics, pp 3685–3694. https://doi.org/10.18653/v1/2020.acl-main.340 . https://aclanthology.org/2020.acl-main.340
5. 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). Association for Computational Linguistics, pp 4755–4766. https://doi.org/10.18653/v1/2021.acl-long.367; https://aclanthology.org/2021.acl-long.367
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