  Joint entity and relation extraction has been a core task in the field of
information extraction. Recent approaches usually consider the extraction of
relational triples from a stereoscopic perspective, either learning a
relation-specific tagger or separate classifiers for each relation type.
However, they still suffer from error propagation, relation redundancy and lack
of high-level connections between triples. To address these issues, we propose
a novel query-based approach to construct instance-level representations for
relational triples. By metric-based comparison between query embeddings and
token embeddings, we can extract all types of triples in one step, thus
eliminating the error propagation problem. In addition, we learn the
instance-level representation of relational triples via contrastive learning.
In this way, relational triples can not only enclose rich class-level semantics
but also access to high-order global connections. Experimental results show
that our proposed method achieves the state of the art on five widely used
benchmarks.
