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Incorporating semantics, syntax and knowledge for aspect based sentiment analysis
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AI
DOI：10.1007/s10489-022-04307-4.png)
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Abstract
En 中文
Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analysis task, whose goal is to identify the sentiment polarity of the specific aspect term in a given sentence. Previous work has realized the importance of commonsense knowledge, semantic and syntax information for aspect-based sentiment analysis, while few approaches take them into account simultaneously. To tackle this problem, we propose a novel graph convolutional network to incorporate commonsense knowledge, syntax and semantics information for this task. Specifically, we first construct an aspect-specific dependency tree rooted at aspect by reshaping an ordinary dependency parse tree and then integrate commonsense knowledge into the refined tree. Based on it, a semantic graph convolutional network is utilized to capture semantics information and a syntax-knowledge graph convolutional network with range-aware weight mechanism is adopted to encode important aspect-relevant commonsense knowledge and syntax information. Finally, an information exchange module is applied to interact commonsense knowledge, syntax and semantics information for classification. Experimental results demonstrate that our proposed model outperforms state-of-the-art models.
Keywords:
Aspect-based sentiment analysis
Graph convolutional network
Commonsense knowledge
BERT