Abstract: As a subtask in the field of natural language processing (NLP), relation extraction aims to identify the relationships between specific entity pairs from unstructured text. Aiming at the problems of incomplete extraction of key semantic features and the introduction of syntactic knowledge accompanied by a large amount of noise information in existing studies on Chinese relation extraction, a dynamic dependency-driven and multiple feature-enhanced Chinese relation extraction model is constructed. The model consists of two channels. In channel one, the original dependency parse trees for entity pairs are reconstructed and dynamically pruned to remove redundant syntactic dependencies, and deep syntactic features are captured through a graph convolutional network (GCN). In channel two, relative position vectors are constructed for entities, and segmented feature extraction is performed on these vectors using segmented convolution to obtain local semantic features. Global semantic features are captured using a hybrid attention mechanism, and local and global semantic features are fused through a gating mechanism. Finally, the feature representations from the two channels are interactively fused. Experimental results demonstrate that the model outperforms baseline models on four public datasets: COAE2016, SanWen, FinRE, and SciRE.  

Key words: relation extraction, graph convolutional network, hybrid attention, entity position information, dynamic dependency