A joint triple extraction method by entity role attribute recognition

In recent years, joint triple extraction methods have received extensive attention because they have significantly promoted the progress of information extraction and many related downstream tasks in the field of natural language processing. However, due to the inherent complexity of language such as relation overlap, joint extraction model still faces great challenges. Most of the existing models to solve the overlapping problem adopt the strategy of constructing complex semantic shared encoding features with all types of relations, which makes the model suffer from redundancy and poor inference interpretability in the prediction process. Therefore, we propose a new model for entity role attribute recognition based on triple holistic fusion features, which can extract triples (including overlapping triples) under a limited number of relationships, and its prediction process is simple and easy explain. We adopt the strategy of low-level feature separation and high-level concept fusion. First, we use the low-level token features to perform entity and relationship prediction in parallel, then use the residual connection with attention calculation to perform feature fusion on the candidate triples in the entity-relation matrix, and finally determine the existence of triple by identifying the entity role attributes. Experimental results show that the proposed model is very effective and achieves state-of-the-art performance on the public datasets.