A Semantic Feasibility-Constrained NLI Framework for Scientific Relation Extraction
Abstract
Scientific relation extraction aims to identify fine-grained semantic relations between domain-specific entities in scientific documents. However, complex scientific expressions, domain adaptation challenges, and subtle relation distinctions make this task difficult. To address these challenges, we propose a novel natural language inference-based framework for scientific relation extraction. The method reformulates relation classification as textual entailment prediction by converting each candidate relation into a natural language hypothesis and pairing it with a premise constructed from the original scientific context. To exploit the hierarchical semantics of pretrained language models, we introduce an inter-layer semantic attention fusion module that adaptively aggregates contextual representations from multiple layers instead of relying only on the final layer. Moreover, we design a semantic feasibility-constrained focal contrastive learning strategy to reduce the interference of semantically infeasible negative hypotheses and strengthen the learning of hard-to-distinguish relation instances. Experiments on the public SciER dataset show that our framework outperforms strong supervised and existing NLI-based baselines, demonstrating its effectiveness and competitive performance.