Aspect sentiment triple extraction for grammar-weighted graph text

Aspect sentiment triple extraction includes three tasks: aspect term extraction, opinion term extraction, and aspect sentiment classification. However, research methods that solve this task in a pipeline way cannot utilize the interaction information between elements, and will also cause error propagation and redundant training. To solve the above problems, an aspect sentiment triple extraction method based on gated attention and weighted graph text is proposed, which makes full use of the semantic and grammatical relationships between triple elements to enhance element interaction. Firstly, the model uses a bidirectional long-short-term memory network to learn the sequence feature representation of sentences. Secondly, a gated attention unit is used to learn linear connections between words. Thirdly, a grammatical distance-weighted graph convolutional network is employed to enhance the interactions between triplet elements. Finally, a grid tagging inference strategy is applied to predict triples. Experimental results on four public datasets show that the proposed method can effectively enhance the interaction between triple elements and improve the accuracy of triple extraction. Moreover, the F₁ values of the proposed method are 57.94%, 70.54%, 61.95% and 67.66%, respectively, which are all improved compared to the baseline model.