CoT-CC: chain-of-thought reasoning and contrastive correction for zero-shot medical relation extraction

Medical relation extraction converts unstructured medical texts into structured triples by identifying entities and their semantic relations. However, high annotation costs, limited supervision, and long-tailed relation distributions make zero-shot medical relation extraction particularly challenging, as models need to recognize unseen relation types without relation-specific training examples. Existing methods mainly rely on direct fine-tuning or semantic matching, providing limited supervision for the intermediate decisions that determine the final extraction results. We propose CoT-CC, a Chain-of-Thought reasoning and Contrastive Correction framework for zero-shot medical relation extraction. CoT-CC first distills a four-stage reasoning process consisting of Entity Locator, Entity Classifier, Relation Extractor, and Validator, thereby making entity localization, entity classification, relation inference, and result verification explicit. It then retrieves semantically relevant positive examples and dynamically constructs rule-constrained negative examples targeting two common errors: entity boundary errors and head-tail reversals. By contrasting the initial prediction with these positive and negative references, the correction module refines incorrect entity selections and relation directions before producing the final triples. Experiments on CIC and CMeIE show that CoT-CC achieves better overall performance than representative baselines and shows stable performance across different numbers and partitions of unseen relation types.