A mutually-exclusive binary cross tagging framework for joint extraction of entities and relations

Joint extraction from unstructured text aims to extract relational triples composed of entity pairs and their relations. However, most existing works fail to process the overlapping issues that occur when the same entities are utilized to generate different relational triples in a sentence. In this work, we propose a mutually exclusive Binary Cross Tagging (BCT) scheme and develop the end-to-end BCT framework to jointly extract overlapping entities and triples. Each token of entities is assigned a mutually exclusive binary tag, and then these tags are cross-matched in all tag sequences to form triples. Our method is compared with other state-of-the-art models in two English public datasets and a large-scale Chinese dataset. Experiments show that our proposed framework achieves encouraging performance in F1 scores for the three datasets investigated. Further detailed analysis demonstrates that our method achieves strong performance overall with three overlapping patterns, especially when the overlapping problem becomes complex.

Automated summary

This paper proposes a mutually exclusive Binary Cross Tagging (BCT) scheme and develops an end-to-end BCT framework to jointly extract overlapping entities and triples. The proposed framework achieves encouraging performance in F1 scores on multiple datasets, especially when the overlapping problem becomes complex.