Interactive Attention Encoder Network with Local Context Features for Aspect-Level Sentiment Analysis

Wang, Ruyan; Tao, Zhongyuan

doi:10.1109/iccc49849.2020.9238924

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…Qiang et al [23] implemented a new multiattention network that extracted sentiment features with self-and position-aware attention mechanism. Wang et al [24] extracted global context information using an interactive attentional coding network. Inspired by this, we used multi-head attention to obtain the interaction information between aspect and context, and then we used bidirectional attention to obtain further interaction information.…”

Section: Related Workmentioning

confidence: 99%

Graph Convolutional Networks with Bidirectional Attention for Aspect-Based Sentiment Classification

Liu

Zhu

et al. 2021

Applied Sciences

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Aspect-based sentiment classification aims at determining the corresponding sentiment of a particular aspect. Many sophisticated approaches, such as attention mechanisms and Graph Convolutional Networks, have been widely used to address this challenge. However, most of the previous methods have not well analyzed the role of words and long-distance dependencies, and the interaction between context and aspect terms is not well realized, which greatly limits the effectiveness of the model. In this paper, we propose an effective and novel method using attention mechanism and graph convolutional network (ATGCN). Firstly, we make full use of multi-head attention and point-wise convolution transformation to obtain the hidden state. Secondly, we introduce position coding in the model, and use Graph Convolutional Networks to obtain syntactic information and long-distance dependencies. Finally, the interaction between context and aspect terms is further realized by bidirectional attention. Experiments on three benchmarking collections indicate the effectiveness of ATGCN.

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Section: Related Workmentioning

confidence: 99%