Knowledge Enhancement and Semantic Information-Fused Emotion–Cause Pair Extraction

Emotion–cause pair extraction is a crucial task in natural language processing that identifies emotional expressions and their corresponding causes within text. Despite substantial progress, most current approaches depend on sequence modeling or standard attention mechanisms, which frequently overlook intricate inter-sentential relationships and fail to utilize causal commonsense knowledge to enhance semantic links between clauses. To address these limitations, this paper introduces KESIF, a novel emotion–cause pair extraction model that integrates knowledge enhancement with enriched semantic information for improved performance. The proposed model incorporates a graph attention network to capture semantic dependency relationships between sentences, integrates causal commonsense knowledge from the ATOMIC knowledge base to enrich semantic representations, and utilizes a bidirectional MRC mechanism for achieving effective bidirectional matching between emotions and causes. The model’s performance is assessed using core metrics, such as precision, recall, and F1 score. Experimental results on both Chinese and English datasets demonstrate that our method outperforms SOTA baselines.