Aspect sentiment triplet extraction incorporating syntactic constituency parsing tree and commonsense knowledge graph

• Main Authors: HU, Zhenda, WANG, Zhaoxia, WANG, Yinglin, TAN, Ah-hwee
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2023
• Subjects: Aspect sentiment triplet extraction; Syntactic constituency parsing tree; Commonsense knowledge graph; Graph convolutional network; Artificial Intelligence and Robotics
• Online Access: https://ink.library.smu.edu.sg/sis_research/7758; https://ink.library.smu.edu.sg/context/sis_research/article/8761/viewcontent/Aspect_Sentiment_Triplet_Extraction_Incorporating_Syntactic_Constituency_Parsing_Tree_and_Commonsense_Knowledge_Graph.pdf
• Institution: Singapore Management University

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.