Position-Aware Tagging for Aspect Sentiment Triplet Extraction
This addresses the problem of extracting structured sentiment information from text for natural language processing applications, representing an incremental improvement over prior methods.
The paper tackled Aspect Sentiment Triplet Extraction (ASTE) by proposing a joint model with a position-aware tagging scheme to capture interactions among target entities, sentiments, and opinion spans, resulting in improved performance over existing pipeline approaches on several datasets.
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.