PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
This addresses inefficiencies and overlapping issues in information extraction for NLP applications, representing an incremental improvement over existing methods.
The paper tackles joint extraction of entities and relations from text by decomposing it into relation judgement, entity extraction, and subject-object alignment, proposing the PRGC framework that achieves state-of-the-art performance on public benchmarks with higher efficiency.
Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint relational triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples.