Knowledge-guided multi-granularity GCN for ABSA |
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| Institution: | 1. Department of Computer Science & Artificial Intelligence, Universidad de Granada, Spain;2. Department of Software Engineering, Universidad de Granada, Spain;3. School of Computer Science and Engineering, Nanyang Technological University, Singapore;1. Nanjing University of Aeronautics and Astronautics, Nanjing, China;2. Hohai University, Changzhou, China;1. School of Management and Economics, Beijing Institute of Technology, Beijing, China;2. Smart Tourism Research Center, Kyung Hee University, Seoul, South Korea;1. Department of Geography, University of the Balearic Islands, Palma, Balearic Islands, Spain;2. Department of Applied Economics, University of the Balearic Islands, Palma, Balearic Islands, Spain;3. Department of Industrial Engineering and Construction, University of the Balearic Islands, Spain, Balearic Islands, Spain |
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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. |
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| Keywords: | Sentiment analysis Graph neural network Conceptual knowledge Robustness analysis Attention mechanism |
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