Entity Overlapping Relation Extracting Algorithm based on CNN and BERT

Knowledge graphs show excellent application potential in natural language processing, but extracting overlapping relations of entities in their construction poses a significant challenge. This paper proposes an entity overlapping relation extraction algorithm based on a one-dimensional convolutional neural network, which combines the local features of convolution and the context features of sequence, enhances the feature concentration of data, and uses a cascaded decoding framework to solve the problem of overlapping relation extraction effectively. The feasibility of the proposed method was verified on two public NYT and WebNLG English datasets, and the experimental results show that the F1-score values of this algorithm were improved by 1.9% and 0.6%, respectively, significantly superior to similar algorithms.