BERT-PAGG: a Chinese relationship extraction model fusing PAGG and entity location information

Relationship extraction is one of the important tasks of constructing knowledge graph. In recent years, many scholars have introduced external information other than entities into relationship extraction models, which perform better than traditional relationship extraction methods. However, they ignore the importance of the relative position between entities. Considering the relative position between entity pairs and the influence of sentence level information on the performance of relationship extraction model, this article proposes a BERT-PAGG relationship extraction model. The model introduces the location information of entities, and combines the local features extracted by PAGG module with the entity vector representation output by BERT. Specifically, BERT-PAGG integrates entity location information into local features through segmented convolution neural network, uses attention mechanism to capture more effective semantic features, and finally regulates the transmission of information flow through gating mechanism. Experimental results on two open Chinese relation extraction datasets show that the proposed method achieves the best results compared with other models. At the same time, ablation experiments show that PAGG module can effectively use external information, and the introduction of this module makes the Macro-F1 value of the model increase by at least 2.82%.