基于双重监督对比学习的观点目标抽取OA

With the rapid development of social media and e-commerce platforms,massive user reviews have be-come a vital source of feedback on products and services.Opinion target extraction is a key task in opinion mining,aiming to identify the specific objects that users evaluate in review texts.The major challenge of this task lies in the fact that users often express their opinions using both explicit and implicit manners,while existing methods achieve limited effectiveness in recognizing implicit opinion targets.To address this problem,this paper proposes a dual-supervised contrastive learning-based opinion target extraction model(DCLWS),which enhances the model's dis-criminative ability by leveraging the semantic correlation between explicit and implicit targets.This model inte-grates intra-sentence contextual information and cross-sentence semantic information to construct a contrastive learning framework:using review sentences containing explicit opinion targets as anchor samples,review sentences corresponding to implicit targets of the same category as positive samples,and those corresponding to implicit tar-gets of different categories as negative samples,thereby guiding the model to learn more discriminative target word representations and sentence-level semantic representations.Experimental results on four benchmark datasets from the SemEval ABSA 2014,2015 and 2016 challenges demonstrate that the proposed model achieves significant per-formance advantages over existing state-of-the-art models,with an F1 score reaching up to 89.20%;in the implicit opinion target recognition subtask,the precision is improved to 98.32%.The results validate the effectiveness and robustness of the model in complex linguistic scenarios,providing a reliable approach for enhancing the perfor-mance of opinion mining systems in practical applications.