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Abstract
Aspect term extraction is a crucial step in aspect-level sentiment analysis, significantly affecting the accuracy of sentiment classification. Therefore, improving the precision of aspect term extraction is vital for enhancing the performance of sentiment analysis. The limitations of existing methods include inadequate consideration of syntactic information and inter-word dependencies, as well as the challenge of mitigating weight noise during dependency tree conversion. To address these issues, we propose an aspect term extraction approach that leverages dynamic attention and graph convolutional network. Our method utilizes a densely connected graph convolutional network to capture dependency information between distant terms, thereby enriching vector semantics. Furthermore, it integrates a dynamic attention mechanism informed by dependency parsing to highlight critical dependencies and mitigate noise interference. We benchmark our model against state-of-the-art approaches on four widely used public datasets. The results indicate that our proposed method significantly enhances the performance of aspect term extraction. Specifically, our model improves upon baseline models on the Lap14 and Rest15 datasets, with increases in macro-F1 scores of 0.45, and 0.04, respectively.
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Acknowledgments
This paper is supported by the National Key Research and Development Program of China (2022YFC3303501) and Monitoring and Early Warning Service Platform Project (2023-275-1-1).
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Authors and Affiliations
School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China
Xin Sun, Yongqing Mi & Hongao Li
Center for Educational Technology and Resource Development, Ministry of Education, Beijing, P.R. China
Jia Liu
National Center for Educational Technology, NCET, Beijing, 100031, China
Jia Liu
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Correspondence toXin Sun.
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Kyoto University, Kyoto, Japan
Rafik Hadfi
Lincoln University, Christchurch, New Zealand
Patricia Anthony
RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
Alok Sharma
Kyoto University, Kyoto, Japan
Takayuki Ito
University of Tasmania, Tasmania, TAS, Australia
Quan Bai
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Sun, X., Mi, Y., Liu, J., Li, H. (2025). Aspect Term Extraction via Dynamic Attention and a Densely Connected Graph Convolutional Network. In: Hadfi, R., Anthony, P., Sharma, A., Ito, T., Bai, Q. (eds) PRICAI 2024: Trends in Artificial Intelligence. PRICAI 2024. Lecture Notes in Computer Science(), vol 15281. Springer, Singapore. https://doi.org/10.1007/978-981-96-0116-5_32
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