  Joint entity and relation extraction is an essential task in natural language
processing and knowledge graph construction. Existing approaches usually
decompose the joint extraction task into several basic modules or processing
steps to make it easy to conduct. However, such a paradigm ignores the fact
that the three elements of a triple are interdependent and indivisible.
Therefore, previous joint methods suffer from the problems of cascading errors
and redundant information. To address these issues, in this paper, we propose a
novel joint entity and relation extraction model, named OneRel, which casts
joint extraction as a fine-grained triple classification problem. Specifically,
our model consists of a scoring-based classifier and a relation-specific horns
tagging strategy. The former evaluates whether a token pair and a relation
belong to a factual triple. The latter ensures a simple but effective decoding
process. Extensive experimental results on two widely used datasets demonstrate
that the proposed method performs better than the state-of-the-art baselines,
and delivers consistent performance gain on complex scenarios of various
overlapping patterns and multiple triples.
