MPBE: Multi-perspective boundary enhancement network for aspect sentiment triplet extraction

Aspect Sentiment Triple Extraction (ASTE) is an emerging task in sentiment analysis that aims to extract triplets consisting of aspect terms, opinion terms, and sentiment polarity from review texts. Previous span-based methods often struggle with accurately identifying the boundaries of aspect and opinion terms, especially when multiple word spans appear in a sentence. This limitation arises from their reliance on a single, simplistic approach to constructing contextual features. To address these challenges, we propose Multi-Perspective Boundary Enhancement Network (MPBE). The network captures rich contextual features by adopting a dual-encoder mechanism and constructs multiple channels to further enhance these features. Specifically, we introduce enhanced semantic and syntactic information in two channels, while the third channel transforms the features using discrete fourier transform. In addition, we design a dual-graph cross fusion module to fuse features from different channels for more efficient information interaction and integration. Finally, by statistically analyzing the length distribution of aspect and opinion terms, a candidate length-based decoding strategy is proposed to achieve more accurate decoding. In experiments, the proposed MPBE model achieved excellent results on four benchmark datasets (14Lap, 14Res, 15Res, 16Res), with F1 scores of 62.32%, 73.78%, 65.32%, and 73.36%, respectively, demonstrating the superiority of the method.