Aspect Sentiment Triplet Extraction Incorporating Syntactic Constituency Parsing Tree and Commonsense Knowledge Graph

作者：Hu, Zhenda; Wang, Zhaoxia; Wang, Yinglin^*; Tan, Ah-Hwee

来源：Cognitive Computation, 2023, 15(1): 337-347.

DOI：10.1007/s12559-022-10078-4

• Aspect sentiment triplet extraction
• Syntactic constituency parsing tree
• Commonsense knowledge graph
• Graph convolutional network

The aspect sentiment triplet extraction (ASTE) task aims to extract the target term and the opinion term, and simultaneously identify the sentiment polarity of target-opinion pairs from the given sentences. While syntactic constituency information and commonsense knowledge are both important and valuable for the ASTE task, only a few studies have explored how to integrate them via flexible graph convolutional networks (GCNs) for this task. To address this gap, this paper proposes a novel end-to-end model, namely GCN-EGTS, which is an enhanced Grid Tagging Scheme (GTS) for ASTE leveraging syntactic constituency parsing tree and a commonsense knowledge graph based on GCNs. Specifically, two types of GCNs are developed to model the information involved, namely span GCN for syntactic constituency parsing tree and relational GCN (R-GCN) for commonsense knowledge graph. In addition, a new loss function is designed by incorporating several constraints for GTS to enhance the original tagging scheme. The extensive experiments on several public datasets demonstrate that GCN-EGTS outperforms the state-of-the-art approaches significantly for the ASTE task based on the evaluation metrics. The outcomes of this research indicate that effectively incorporating syntactic constituency parsing information and commonsense knowledge is a promising direction for the ASTE task.