Dual-channel interaction learning with a span-level for aspect sentiment triplet extraction

Xiajiong Shen; Huijing Yang; Yiru Han; Yunsong Li; Xianjin Shi

doi:10.1117/12.3005942

10 October 2023 Dual-channel interaction learning with a span-level for aspect sentiment triplet extraction

Xiajiong Shen, Huijing Yang, Yiru Han, Yunsong Li, Xianjin Shi

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127991G (2023) https://doi.org/10.1117/12.3005942
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

Aspect Sentiment Triple Extraction (ASTE) is a new subtask for fine-grained sentiment analysis aimed to extract the triples of aspect, opinion and sentiment terms from sentences. Previous work has extracted the target triple in an end-to-end approach, but such rely significantly on the interactions between aspect words and opinion words. Thus, they do not handle well the aspect and opinion terms containing multiple words. Recently, the span-level based models have performed well on ASTE tasks by utilizing the prediction of all possible spans. In this paper, we propose a span-level dual-channel interactive learning network for extracting target triples which uses all possible spans in a sentence as input and dual extraction of target terms using a target-aware attention mechanism to complement each other. Our model makes some contributions to both ASTE as well as ATE and OTE tasks. Experimental results show that the framework achieves better performance in extracting triples.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xiajiong Shen, Huijing Yang, Yiru Han, Yunsong Li, and Xianjin Shi "Dual-channel interaction learning with a span-level for aspect sentiment triplet extraction", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127991G (10 October 2023); https://doi.org/10.1117/12.3005942

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