Multiple-level Enhanced Graph Convolutional Network for Aspect Sentiment Triplet Extraction

Abstract

Aspect Sentiment Triplet Extraction (ASTE) is a method for extracting aspect terms, opinion terms, and their corresponding sentiment polarities from a given sentence. Most of the existing studies use joint extraction methods to extract the triplets directly in a unified framework. However, most joint extraction methods only consider the semantic and syntactic dependency information of the sentence. Due to a lack of sentiment information and positional information, they are unable to accurately and completely express the aspect and opinion in the sentence. In order to solve the above problems, we introduce a Multiple-level Enhanced Graph Convolutional Network (MEGCN) for ASTE, which utilizes sentiment scores and sentiment polarity nodes alongside syntactic dependency information. This approach not only enriches contextual understanding by integrating sentiment data but also improves positional analysis of aspect and opinion terms through polarity nodes. Moreover, our dual-aware fusion module, combining semantic with sentiment-enhanced syntactic features through a biaffine attention mechanism and matrix construction, enables a deeper representation of aspect sentiment triplets. Our model demonstrates superior performance over existing methods on two widely recognized ASTE datasets.