  Relation classification aims to predict a relation between two entities in a
sentence. The existing methods regard all relations as the candidate relations
for the two entities in a sentence. These methods neglect the restrictions on
candidate relations by entity types, which leads to some inappropriate
relations being candidate relations. In this paper, we propose a novel
paradigm, RElation Classification with ENtity Type restriction (RECENT), which
exploits entity types to restrict candidate relations. Specially, the mutual
restrictions of relations and entity types are formalized and introduced into
relation classification. Besides, the proposed paradigm, RECENT, is
model-agnostic. Based on two representative models GCN and SpanBERT
respectively, RECENT_GCN and RECENT_SpanBERT are trained in RECENT.
Experimental results on a standard dataset indicate that RECENT improves the
performance of GCN and SpanBERT by 6.9 and 4.4 F1 points, respectively.
Especially, RECENT_SpanBERT achieves a new state-of-the-art on TACRED.
