  Aspect sentiment triplet extraction (ASTE) is a crucial subtask of
aspect-based sentiment analysis (ABSA) that aims to comprehensively identify
sentiment triplets. Previous research has focused on enhancing ASTE through
innovative table-filling strategies. However, these approaches often overlook
the multi-perspective nature of language expressions, resulting in a loss of
valuable interaction information between aspects and opinions. To address this
limitation, we propose a framework that leverages both a basic encoder,
primarily based on BERT, and a particular encoder comprising a Bi-LSTM network
and graph convolutional network (GCN ). The basic encoder captures the
surface-level semantics of linguistic expressions, while the particular encoder
extracts deeper semantics, including syntactic and lexical information. By
modeling the dependency tree of comments and considering the part-of-speech and
positional information of words, we aim to capture semantics that are more
relevant to the underlying intentions of the sentences. An interaction strategy
combines the semantics learned by the two encoders, enabling the fusion of
multiple perspectives and facilitating a more comprehensive understanding of
aspect--opinion relationships. Experiments conducted on benchmark datasets
demonstrate the state-of-the-art performance of our proposed framework.
