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Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis

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Intelligent Information Processing XII (IIP 2024)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 704))

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Abstract

Previous studies consider little on using dependency-type messages in the E2E-ABSA task. Studies using dependency-type messages just contact the dependency-type message and word embedding vectors, which may not fully fuse the context feature and information from the dependency type. This paper proposes a new model called Dependency-Type Weighted Graph Convolution Network (DTW-GCN) to compose dependency-type messages and word embedding. We use a type-weighted matrix to combine the dependency-type message, and DTW-GCN could fuse the dependency-type message and word embedding vectors. Experiments conducted on three benchmark datasets verify the effectiveness of our model.

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Authors and Affiliations

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

    Yusong Mu & Shumin Shi

  2. Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications, Beijing, China

    Shumin Shi

Authors
  1. Yusong Mu
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  2. Shumin Shi
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Correspondence to Yusong Mu or Shumin Shi .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Zhongzhi Shi

  2. University of Oslo, Oslo, Norway

    Jim Torresen

  3. De Montfort University, Leicester, UK

    Shengxiang Yang

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Mu, Y., Shi, S. (2024). Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis. In: Shi, Z., Torresen, J., Yang, S. (eds) Intelligent Information Processing XII. IIP 2024. IFIP Advances in Information and Communication Technology, vol 704. Springer, Cham. https://doi.org/10.1007/978-3-031-57919-6_4

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  • DOI: https://doi.org/10.1007/978-3-031-57919-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-57918-9

  • Online ISBN: 978-3-031-57919-6

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