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| Word-Pair Relation Learning Method for Aspect Sentiment Triplet Extraction |
| XIA Hongbin1,2, LI Qiang1, XIAO Yifei1 |
1. School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122;
2. Jiangsu Key Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi 214122 |
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Abstract Aspect sentiment triplet extraction is designed to identify aspect items with their sentiment tendencies in a comment and to extract the related opinion items. In most of the existing methods, this type of task is divided into several sub-tasks, and then the task is completed by the pipeline composed of the sub-tasks. However, the methods based on pipeline are affected by error propagation and inconvenience for use in practice. Therefore, a word-pair relation learning method for aspect sentiment triplet extraction is proposed, which transforms the aspect sentiment triplet extraction task into an end-to-end word-pair relation learning task. The method contains a word-pair relation tagging scheme, which can unify word-pair relations in sentences to represent all triplets, and a specially built word-pair relation network to output word-pair relation. Firstly, the sentence is encoded by bidirectional grated recurrent unit and mixed attention. Then, sentence coding is converted into tag probabilities through the attention map transform module. Finally, the triplets are extracted from the result of the word-pair relation tag. In addition, the pre-trained bidirectional encoder representation from transformer is applied to the proposed method. Experiments on four standard datasets show that the proposed method is superior.
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Received: 26 October 2021
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| Fund:National Natural Science Foundation of China(No.61972182) |
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Corresponding Authors:
XIA Hongbin, Ph.D., associate professor. His research interests include personalized recommendation, natural language processing and network optimization.
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About author:: LI Qiang, master student. His research interests include natural language processing and machine learning.
XIAO Yifei, master student. His research interests include natural language processing. |
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[1] PANG B, LEE L.Opinion Mining and Sentiment Analysis. Foundations and Trends in Information Retrieval, 2008, 2(1/2): 1-135.
[2] ORTIGOSA A, MARTÍN J M, CARRO R M.Sentiment Analysis in Facebook and Its Application to e-Learning. Computers in Human Behavior, 2014, 31: 527-541.
[3] XUE W, ZHOU W B, LI T, ,et al. MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction on Restaurant Reviews // Proc of the 8th International Joint Confe-rence on Natural Language Processing. Stroudsburg, USA: ACL, 2017, II: 151-156.
[4] YANG Y Y, LI K, QUAN X J, et al. Constituency Lattice Encoding for Aspect Term Extraction // Proc of the 28th International Conference on Computational Linguistics. Stroudsburg,USA: ACL, 2020: 844-855.
[5] TANG D Y, QIN B, FENG X C, et al. Effective LSTMs for Target-Dependent Sentiment Classification // Proc of the 26th International Conference on Computational Linguistics: Technical Papers. Strouds-burg,USA: ACL, 2016: 3298-3307.
[6] RUDER S, GHAFFARI P, BRESLIN J G.A Hierarchical Model of Reviews for Aspect-Based Sentiment Analysis // Proc of the Confe-rence on Empirical Methods in Natural Language Processing. Stroudsburg,USA: ACL, 2016: 999-1005.
[7] WANG Y Q, HUANG M L, ZHU X Y, et al. Attention-Based LSTM for Aspect-Level Sentiment Classification // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg,USA: ACL, 2016: 606-615.
[8] LIU J M, ZHANG Y. Attention Modeling for Targeted Sentiment // Proc of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Short Papers. Stroudsburg, USA: ACL, 2017, II: 572-577.
[9] ZHANG Y H, QI P, MANNING C D.Graph Convolution over Pruned Dependency Trees Improves Relation Extraction // Proc of the Conference on Empirical Methods in Natural Language Proce-ssing. Stroudsburg,USA: ACL, 2018: 2205-2215.
[10] WANG K, SHEN W Z, YANG Y Y, et al. Relational Graph Attention Network for Aspect-Based Sentiment Analysis // Proc of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg,USA: ACL, 2020: 3229-3238.
[11] LI X, BING L D, LI P J, et al. A Unified Model for Opinion Target Extraction and Target Sentiment Prediction. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 6714-6721.
[12] HE R D, LEE W S, NG H T, et al. An Interactive Multi-task Learning Network for End-to-End Aspect-Based Sentiment Analysis // Proc of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg,USA: ACL, 2019: 504-515.
[13] PENG H Y, XU L, BING L D, et al. Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Ana-lysis. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(5): 8600-8607.
[14] CHEN Z, QIAN T Y.Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis // Proc of the 58th Annual Meeting of the Association for Computational Linguistics. Stroudsburg,USA: ACL, 2020: 3685-3694.
[15] CHEN S W, WANG Y, LIU J, et al. Bidirectional Machine Rea-ding Comprehension for Aspect Sentiment Triplet Extraction. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(14): 12666-12674.
[16] JIAN S Y B, NAYAK T, MAJUMDER N, et al. Aspect Sentiment Triplet Extraction Using Reinforcement Learning[C/OL].[2021-09-18]. https://arxiv.org/pdf/2108.06107.pdf.
[17] XU L, CHIA Y K, BING L D.Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction // Proc of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Proce-ssing. Stroudsburg,USA: ACL, 2021: 4755-4766.
[18] XU L, LI H, LU W, et al. Position-Aware Tagging for Aspect Sentiment Triplet Extraction // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg,USA: ACL, 2020: 2339-2349.
[19] WU Z, YING C C, ZHAO F, et al. Grid Tagging Scheme for Aspect-Oriented Fine-Grained Opinion Extraction // Proc of the Conference on Empirical Methods in Natural Language Processing. Stroudsburg,USA: ACL, 2020: 2576-2585.
[20] JIANG Z H, YU W H, ZHOU D Q, et al. ConvBERT: Improving BERT with Span-Based Dynamic Convolution[C/OL].[2021-09-18]. https://arxiv.org/pdf/2008.02496v3.pdf.
[21] VASWANI A, SHAZEER N, PARMAR N, et al.Attention Is All You Need // Proc of the 31st International Conference on Neural Information Processing Systems. Cambridge, USA: The MIT Press, 2017: 6000-6010.
[22] PONTIKI M, GALANIS D, PAVLOPOULOS J, et al. SemEval-2014 Task 4: Aspect Based Sentiment Analysis // Proc of the 8th International Workshop on Semantic Evaluation. Stroudsburg,USA: ACL, 2014: 27-35.
[23] PONTIKI M, GALANIS D, PAPAGEORGIOU H, et al. SemEval-2015 Task 12: Aspect Based Sentiment Analysis // Proc of the 9th International Workshop on Semantic Evaluation. Stroudsburg,USA: ACL, 2015: 486-495.
[24] PONTIKI M, GALANIS D, PAPAGEORGIOU H, et al. SemEval-2016 Task 5: Aspect Based Sentiment Analysis // Proc of the 10th International Workshop on Semantic Evaluation. Stroudsburg,USA: ACL, 2016: 19-30.
[25] DAI H L, SONG Y Q.Neural Aspect and Opinion Term Extraction with Mined Rules as Weak Supervision // Proc of the 57th Annual Meeting of the Association for Computational Linguistics. Stroudsburg,USA: ACL, 2019: 5268-5277.
[26] WANG W Y, PAN S J, DAHLMEIER D, et al. Coupled Multi-layer Attentions for Co-extraction of Aspect and Opinion Terms // Proc of the 31st AAAI Conference on Artificial Intelligence. Palo Alto,USA: AAAI, 2017: 3316-3322. |
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2022, Vol. 35 
