Enhancing aspect and opinion terms semantic relation for aspect sentiment triplet extraction

Aspect sentiment triplet extraction is the most recent subtask of aspect-based sentiment analysis, which aims to extract triplets information from a review sentence, including an aspect term, corresponding sentiment polarity, and associated opinion expression. Although existing researchers adopt an end-to-end method to avoid the error propagation caused by the pipeline manner, they cannot effectively establish the semantic association between aspects and opinions when extracting triples. Furthermore, utilizing sequence tagging methods in extraction and classification tasks will lead to problems, such as increased model search space and sentiment inconsistency of multi-word entities. To tackle the above issues, we propose an enhancing aspect and opinion terms semantic relation framework to make extract triplets more exact by fully capturing interactive information. Specifically, dual convolutional neural networks are used to construct aspect-oriented and opinion-oriented features respectively, the semantic relation is considered through the attention mechanism, and then feedback to each extraction task. We also employ a span-based tagging scheme to extract multiple entities directly under the supervision of span boundary detection accurately predict sentiment polarity based on span distance. We conduct extensive experiments on four benchmark datasets, and the experimental results demonstrate that our model significantly outperforms all baseline methods.