杨春霞,宋金剑,姚思诚. 面向方面级情感分析的加权依存树卷积网络[J]. 中文信息学报, 2022, 36(5): 125-132.
YANG Chunxia, SONG Jinjian, YAO Sicheng. A Weighted Dependency Tree Convolutional Networks for Aspect-Based Sentiment Analysis. , 2022, 36(5): 125-132.
A Weighted Dependency Tree Convolutional Networks for Aspect-Based Sentiment Analysis
YANG Chunxia1,2,3, SONG Jinjian1,2,3, YAO Sicheng1,2,3
1.Automation Institute,Nanjing University of Information Science & Technology, Nanjing, Jiangsu 210044, China; 2.Jiangsu Key Laboratory of Big Data Analysis Technology (B-DAT), Nanjing, Jiangsu 210044, China; 3.Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing, Jiangsu 210044, China
Abstract：For aspect-based sentiment analysis, existing rule-based dependency tree pruning methods have the problem of deleting some useful information. In addition, how to use the graph convolutional network to obtain the rich global information in the graph structure is also an important problem at present. For the first problem, we use the multi-head attention mechanism to automatically learn how to selectively focus on the structural information that is useful for the classification task, and transform the original dependency tree into a fully connected edge weighted graph.To solve the second problem, we paper introduces dense connections into the graph convolutional network, so that the graph convolutional network can capture rich local and global information. The experimental results on the three public datasets show that the accuracy and F1 of the proposed model are both improved compared with the baseline model.
[1] Yang J, Yang R, Lu H, et al. Multi-entity aspect-based sentiment analysis with context, entity, aspect memory and dependency information[J]. ACM Transactions on Asian and Low-Resource Language Information Processing. 2019,28(4): 1-22. [2] Phan M H, Ogunbona P O. Modelling context and syntactical features for aspect-based sentiment analysis[C]//Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics,2020: 3211-3220. [3] Wang K, Shen W, Yang Y, et al. Relational graph attention network for aspect-based sentiment analysis[C]//Processding of the 58th Annual Meeting of the Association for Computational Linguisties.2020: 3229-3238. [4] Zhang C, Li Q, Song D. Aspect-based sentiment classification with aspect-specific graph convolutional networks[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, Hong Kong: ACL, 2019: 4560-4570. [5] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[C]//Proceedings of the 5th International Conference on Learning Representations. Toulon, France, 2017. [6] Li Q, Han Z, Wu X M. Deeper insights into graph convolutional networks for semi-supervised learning[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Association for the Advancement of Artificial Intelligence, 2018: 3538-3545. [7] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]//Proceedings of the Advances in Neural Information Processing Systems, 2017: 5998-6008. [8] Huang G, Liu Z, Van D M L, et al. Densely connected convolutional networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii: IEEE, 2017: 4700-4708. [9] 刘倩,李宁,田英爱. 面向机器学习的流式文档逻辑结构标注方法研究[J]. 中文信息学报, 2019, 33(9): 50-59,78. [10] 热西旦木·吐尔洪太,吾守尔·斯拉木,伊尔夏提·吐尔贡. 词典与机器学习方法相结合的维吾尔语文本情感分析[J]. 中文信息学报, 2017, 31(1): 177-183. [11] Tai K S, Socher R, Manning C D. Improved semantic representations from tree-structured long short-term memory networks[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing. 2015: 1556-1566. [12] Miwa M, Bansal M. End-to-end relation extraction using LSTMs on sequences and tree structures[C]//Proceedings of the 54th Annual Meeting of the Association for ComputationalLinguistics, Berlin, Germany: ACL, 2016: 1105-1116. [13] Zhang Y, Qi P, Manning C D. Graph convolution over pruned dependency trees improves relation extraction[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium: ACL, 2018: 2205-2215. [14] Xu K, Li C, Tian Y, et al. Representation learning on graphs with jumping knowledge networks[C]//Proceedings of the International Conference on Machine Learning, 2018: 5453-5462. [15] Pennington J, Socher R, Manning C D. Glove: global vectors for word representation[C]//Proceedings of the Conference on Empirical Methods in Natural Language Processing, Doha, Qatar: ACL, 2014: 1532-1543. [16] Kiritchenko S, Zhu X, Cherry C, et al. NRC-Canada-2014: detecting aspects and sentiment in customer reviews[C]//Proceedings of the 8th International Workshop on Semantic Evaluation, Dublin, Ireland: ACL, 2014: 437-442. [17] Song Y, Wang J, Jiang T, et al. Attentional encoder network for targeted sentiment classification[J],CoRR, 2019, abs/1902.09314. [18] Zeng B, Yang H, Xu R, et al. LCF: a local context focus mechanism for aspect-based sentiment classification[J] Applied sciences, 2019, 9(16): 3389. [19] He R, Lee W S, Ng H T, et al. Effective attention modeling for aspect-level sentiment classification[C]//Proceedings of the 27th International Conference on Computational Linguistics, USA, 2018: 1121-1131.
2022, Vol. 36 
