Abstract
Relation extraction plays a crucial role in tasks such as text processing and knowledge graph construction. However, existing extraction algorithms struggle to maintain accuracy when dealing with long-distance dependencies between entities and noise interference. To address these challenges, this paper proposes a novel relation extraction method that integrates semantic and syntactic features for handling noisy long-distance dependencies. Specifically, we leverage contextual semantic features generated by the pre-trained BERT model alongside syntactic features derived from dependency syntax graphs, effectively utilizing the complementary strengths of both sources of information to enhance the model's performance in long-distance dependency scenarios. To further improve robustness, we introduce a Self-Attention-based Graph Convolutional Network (SA-GCN) to rank neighboring nodes within the syntactic graph, filtering out irrelevant nodes and capturing long-distance dependencies more precisely in noisy environments. Additionally, a residual shrinking network is incorporated to dynamically remove noise from the syntactic graph, further strengthening the model's noise resistance. Moreover, we propose a loss computation method based on predictive interpolation, which dynamically balances the contributions of semantic and syntactic features through weighted interpolation, thereby enhancing relation extraction accuracy. Experiments conducted on two public relation extraction datasets demonstrate that the proposed method achieves significant improvements in accuracy, particularly in handling long-distance dependencies and noise suppression.