Knowledge-guided multi-granularity GCN for ABSA
Aspect-based sentiment analysis aims to determine sentiment polarities toward specific aspect terms within the same sentence or document. Most recent studies adopted attention-based neural network models to implicitly connect aspect terms with context words. However, these studies were limited by in...
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| Vydáno v: | Information processing & management Ročník 60; číslo 2; s. 103223 |
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| Hlavní autoři: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.03.2023
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| ISSN: | 0306-4573, 1873-5371 |
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| Abstract | Aspect-based sentiment analysis aims to determine sentiment polarities toward specific aspect terms within the same sentence or document. Most recent studies adopted attention-based neural network models to implicitly connect aspect terms with context words. However, these studies were limited by insufficient interaction between aspect terms and opinion words, leading to poor performance on robustness test sets. In addition, we have found that robustness test sets create new sentences that interfere with the original information of a sentence, which often makes the text too long and leads to the problem of long-distance dependence. Simultaneously, these new sentences produce more non-target aspect terms, misleading the model because of the lack of relevant knowledge guidance. This study proposes a knowledge guided multi-granularity graph convolutional neural network (KMGCN) to solve these problems. The multi-granularity attention mechanism is designed to enhance the interaction between aspect terms and opinion words. To address the long-distance dependence, KMGCN uses a graph convolutional network that relies on a semantic map based on fine-tuning pre-trained models. In particular, KMGCN uses a mask mechanism guided by conceptual knowledge to encounter more aspect terms (including target and non-target aspect terms). Experiments are conducted on 12 SemEval-2014 variant benchmarking datasets, and the results demonstrated the effectiveness of the proposed framework.
•This paper proposes a knowledge guided multi-granularity graph convolutional neural network (KMGCN) to solve these problems.•Multi-granularity attention mechanism is designed to enhance the interaction between the aspect terms and the opinion words.•In order to deal with the problem of long distance dependence, KMGCN uses a graph convolutional network that relies on the semantic map based on fine-tuning pre-training.•In particular, KMGCN uses a mask mechanism guided by conceptual knowledge to face with new aspect terms. |
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| AbstractList | Aspect-based sentiment analysis aims to determine sentiment polarities toward specific aspect terms within the same sentence or document. Most recent studies adopted attention-based neural network models to implicitly connect aspect terms with context words. However, these studies were limited by insufficient interaction between aspect terms and opinion words, leading to poor performance on robustness test sets. In addition, we have found that robustness test sets create new sentences that interfere with the original information of a sentence, which often makes the text too long and leads to the problem of long-distance dependence. Simultaneously, these new sentences produce more non-target aspect terms, misleading the model because of the lack of relevant knowledge guidance. This study proposes a knowledge guided multi-granularity graph convolutional neural network (KMGCN) to solve these problems. The multi-granularity attention mechanism is designed to enhance the interaction between aspect terms and opinion words. To address the long-distance dependence, KMGCN uses a graph convolutional network that relies on a semantic map based on fine-tuning pre-trained models. In particular, KMGCN uses a mask mechanism guided by conceptual knowledge to encounter more aspect terms (including target and non-target aspect terms). Experiments are conducted on 12 SemEval-2014 variant benchmarking datasets, and the results demonstrated the effectiveness of the proposed framework.
•This paper proposes a knowledge guided multi-granularity graph convolutional neural network (KMGCN) to solve these problems.•Multi-granularity attention mechanism is designed to enhance the interaction between the aspect terms and the opinion words.•In order to deal with the problem of long distance dependence, KMGCN uses a graph convolutional network that relies on the semantic map based on fine-tuning pre-training.•In particular, KMGCN uses a mask mechanism guided by conceptual knowledge to face with new aspect terms. |
| ArticleNumber | 103223 |
| Author | Zhu, Zhenfang Li, Lin Zhang, Guangyuan Wang, Wenling Li, Kefeng Liu, Peiyu Qi, Jiangtao Zhang, Dianyuan |
| Author_xml | – sequence: 1 givenname: Zhenfang surname: Zhu fullname: Zhu, Zhenfang email: zhuzf@sdjtu.edu.cn organization: School of Information Science and Electrical Engineering, Shandong Jiao Tong University, Jinan 250357, China – sequence: 2 givenname: Dianyuan surname: Zhang fullname: Zhang, Dianyuan email: csdzdy@163.com organization: School of Information Science and Electrical Engineering, Shandong Jiao Tong University, Jinan 250357, China – sequence: 3 givenname: Lin surname: Li fullname: Li, Lin email: cathylilin@whut.edu.cn organization: School of Computer and Artificial Intelligence, Wuhan University of Technology, Wuhan 430070, China – sequence: 4 givenname: Kefeng surname: Li fullname: Li, Kefeng email: 205073@sdjtu.edu.cn organization: School of Information Science and Electrical Engineering, Shandong Jiao Tong University, Jinan 250357, China – sequence: 5 givenname: Jiangtao surname: Qi fullname: Qi, Jiangtao email: qijiangtaohappy@hotmail.com organization: School of Information Science and Electrical Engineering, Shandong Jiao Tong University, Jinan 250357, China – sequence: 6 givenname: Wenling surname: Wang fullname: Wang, Wenling email: 362607793@qq.com organization: Chinese Lexicography Research Center, Lu Dong University, Yantai 264025, China – sequence: 7 givenname: Guangyuan surname: Zhang fullname: Zhang, Guangyuan email: xdzhanggy@163.com organization: School of Information Science and Electrical Engineering, Shandong Jiao Tong University, Jinan 250357, China – sequence: 8 givenname: Peiyu surname: Liu fullname: Liu, Peiyu email: liupy@sdnu.edu.cn organization: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China |
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| Keywords | Sentiment analysis Attention mechanism Conceptual knowledge Robustness analysis Graph neural network |
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| References_xml | – reference: (pp. 3433–3442). – reference: (pp. 214–224). – reference: Fan, F., Feng, Y., & Zhao, D. (2018). Multi-grained attention network for aspect-level sentiment classification. In – reference: Phan, M. H., & Ogunbona, P. O. (2020). Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis. In – reference: Huang, B., & Carley, K. M. (2018). Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification. In – reference: Xue, W., & Li, T. (2018). Aspect Based Sentiment Analysis with Gated Convolutional Networks. In – reference: (pp. 3211–3220). – reference: Ravichandran, D., & Hovy, E. (2002). Learning surface text patterns for a question answering system. 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