Joint Extraction of Entities and Relations Based on Enhanced Span and Gate Mechanism.

Although entity and relation joint extraction can obtain relational triples efficiently and accurately, there are a number of problems; for instance, the information between entity relations could be transferred better, entity extraction based on span is inefficient, and it is difficult to identify nested entities. In this paper, a joint entity and relation extraction model based on an Enhanced Span and Gate Mechanism (ESGM) is proposed to solve the above problems. We design a new span device to solve the problem of entity nesting and inefficiency. We use the pointer network method to predict the beginning and end of the span, and combine them through the one-to-many matching principle. A binary classification model is then trained to predict whether the span of the combination is the subject. In the object prediction stage, a gating unit is added to fuse the subject information with the sentence information and strengthen the information transfer between the entity and the relationship. Finally, the relationship is used as the mapping function to predict the tail entity related to the head entity. Our experimental results prove the effectiveness of this model. The precision of the proposed model reached 93.8% on the NYT dataset, which was 0.4% higher than that of the comparison model. Moreover, when the same experiment was conducted in a nested entity scenario, the accuracy of the proposed model was 4.4% higher than that of the comparison model. [ABSTRACT FROM AUTHOR]