  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.
