2020/10/06 by Lu Xu, Hao Li, Xu, Lu +5 · 9 citations
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Topic Modeling #cs.CL
paper · pdf · doi:10.48550/arxiv.2010.02609
15 pages, 10 figures, accepted by EMNLP 2020
openalex publication_date 2020/10/06 · arxiv created 2021/03/09 · arxiv updated 2021/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. Existing research efforts mostly solve this problem using pipeline approaches, which break the triplet extraction process into several stages. Our observation is that the three elements within a triplet are highly related to each other, and this motivates us to build a joint model to extract such triplets using a sequence tagging approach. However, how to effectively design a tagging approach to extract the triplets that can capture the rich interactions among the elements is a challenging research question. In this work, we propose the first end-to-end model with a novel position-aware tagging scheme that is capable of jointly extracting the triplets. Our experimental results on several existing datasets show that jointly capturing elements in the triplet using our approach leads to improved performance over the existing approaches. We also conducted extensive experiments to investigate the model effectiveness and robustness.