An Enhanced Graph Attention Aggregation Network With Multi‐Level Syntactic and Semantic Prompts for Aspect‐Based Sentiment Triple Extraction

ABSTRACT Aspect‐Based Sentiment Triplet Extraction (ASTE) is one of the hot topics in recent years. Relevant researchers have proposed many neural network models for aspect‐based sentiment triplet extraction. However, they fail to model the complex syntactic and semantic associations between tokens, which limits the synergy of features from different angles, which may lead to inaccurate encoding of the relationship between tokens and thus inaccurate extraction of triples. In response to the problems mentioned above, a graph attention aggregation network with multi‐level Syntactic and Semantic Enhanced Prompts for aspect‐Based sentiment triple extraction (SSEP) is proposed. First, a graph attention relation aggregation module is designed in the context encoding part. Specifically, the module first constructs a relation aggregation graph through the output of the pre‐trained language model, then designs a graph attention aggregator, and finally aggregates the multi‐level output of the pre‐trained language model according to the relation aggregation graph and the graph attention aggregator. Second, a syntactic and semantic enhanced prompt module is proposed. The module uses relation table attention to prompt the model, and then uses a dual‐channel graph neural network to further enhance syntactic and semantic information and prune unimportant information. In addition, a joint boundary detection module is designed. The module can directly extract sentiment triplets using joint boundary detection labels. Finally, experimental results on four public datasets show that SSEP achieves state‐of‐the‐art performance and outperforms other models.