Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction

arXiv:2402.15370v1 Announce Type: cross
Abstract: Aspect Sentiment Triple Extraction (ASTE) is an emerging task in fine-grained sentiment analysis. Recent studies have employed Graph Neural Networks (GNN) to model the syntax-semantic relationships inherent in triplet elements. However, they have yet to fully tap into the vast potential of syntactic and semantic information within the ASTE task. In this work, we propose a \emph{Dual Encoder: Exploiting the potential of Syntactic and Semantic} model (D2E2S), which maximizes the syntactic and semantic relationships among …

abstract analysis arxiv cs.ai cs.cl cs.lg encoder extraction fine-grained gnn graph graph neural networks networks neural networks relationships semantic sentiment sentiment analysis studies syntax type vast