Extracting entity relations from unstructured text is an important step in constructing a knowledge graph, but most current methods are ineffective in dealing with the complex problem of overlapping entities. In this paper, we propose a novel entity relation joint extraction model. It extracts subjects through pointer annotation, fuses the extracted subjects with the sentence vector, then inputs them into an attention layer (Attention Mechanism Based on Relative Position Embedding, AMBRPE) to enhance feature expression ability. Under predefined relation conditions, the model extracts objects corresponding to the extracted subjects to generate relation triplets. And the model can effectively solve the problem of overlapping entities through hierarchical pointer annotation. In addition, we introduce an Adversarial Training Component (ATC) into the model, which generates adversarial samples for training and acts as a text data augmentation method to improve model generalization ability. On the public datasets NYT and WebNLG, we conducted extensive experiments and the results show that our model outperforms the cascaded binary tagging framework (CasRel) by 2.1 and 0.9 percentage points, respectively. Moreover, the effectiveness of the proposed model is verified through ablation experiments.
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Liu et al. (2024) studied this question.