Potential Relationship Based Joint Entity and Relation Extraction

The role of joint entity and relation extraction is to identify entities and their corresponding relations from specific texts, and it is also the basis for constructing and updating knowledge graph. Currently, joint extraction methods ignore information redundancy in the extraction process while pursuing performance. To address this issue, a model based on latent relations for joint entity and relation extraction is proposed. This paper designs a new decoding method to reduce the redundant information of relationships, entities and triples in the prediction process, and it is divided into two steps: extracting potential entity pairs and decoding relationships to complete the extraction of triples. Firstly, the potential entity pair extractor is used to predict whether there is potential relationship between entities, and at the same time, the entity pairs with high confidence are selected as the final potential entity pairs. Secondly, the relational decoding is regarded as a multi-label binary classification task, and the confidence of all relationships between each potential entity pair is predicted by the relational decoder. Finally, the number and type of relationships are determined by confidence to complete the task of extracting triples. Experimental results on two general datasets show that the proposed model is better than the baseline models in terms of accuracy and F1 indicators, which verifies the effectiveness of the proposed model. The ablation experiment also proves the effectiveness of the internal parts of the model.