A word-pair relationship modeling method for aspect-based sentiment information extraction in dialogue text
Aspect-based sentiment analysis aims to capture fine-grained sentiment information con-tained in text and has drawn considerable attention due to its wide applications.However,traditional re-search in aspect-based sentiment analysis predominantly relies on non-interactive review texts,with lim-ited investigation into aspect-based sentiment analysis within interactive dialogue contexts.Addressing this gap,this paper proposes a joint extraction task for aspect-based sentiment information in interactive dialogue scenarios.The task aims to extract complete fine-grained sentiment information triplets consis-ting of target aspects,opinion expressions,and corresponding sentiment polarities,thereby obtaining comprehensive sentiment information from the final utterance in an interactive dialogue.To this end,this paper devises an end-to-end extraction method based on word-pair relation modeling,where in the relationship between word pairs are modeled to map dialogue text onto a directed graph,transforming the decoding process into a search for specific cyclic structures within the graph.To enhance the accura-cy of word-pair relationship modeling,this paper introduces a novel model architecture that integrates relative distance information and dialogue turn information when constructing word-pair relationship representations,and utilizes multi-granularity 2D convolution to enhance interaction between word pairs.Additionally,this paper proposes a dynamic loss weighting method to effectively mitigate the issue of imbalanced category distributions in word-pair relation within dialogue texts.Experimental results demonstrate that,the proposed method outperforms strong baseline methods,achieving an aver-age F1 score improvement of 7.70％and a maximum improvement of 15.05％.
aspect-based sentiment analysisfine-grained sentiment information extractiondialogue textword-pair relationship modeling
万方数据
维普
