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Aspect Term Extraction via Dynamic Attention and a Densely Connected Graph Convolutional Network

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Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 15281))

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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).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Xin Sun, Yongqing Mi & Hongao Li

  2. Center for Educational Technology and Resource Development, Ministry of Education, Beijing, P.R. China

    Jia Liu

  3. National Center for Educational Technology, NCET, Beijing, 100031, China

    Jia Liu

Authors
  1. Xin Sun

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  2. Yongqing Mi

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  3. Jia Liu

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  4. Hongao Li

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Corresponding author

Correspondence toXin Sun.

Editor information

Editors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Rafik Hadfi

  2. Lincoln University, Christchurch, New Zealand

    Patricia Anthony

  3. RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Alok Sharma

  4. Kyoto University, Kyoto, Japan

    Takayuki Ito

  5. 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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