  An exhaustive study has been conducted to investigate span-based models for
the joint entity and relation extraction task. However, these models sample a
large number of negative entities and negative relations during the model
training, which are essential but result in grossly imbalanced data
distributions and in turn cause suboptimal model performance. In order to
address the above issues, we propose a two-phase paradigm for the span-based
joint entity and relation extraction, which involves classifying the entities
and relations in the first phase, and predicting the types of these entities
and relations in the second phase. The two-phase paradigm enables our model to
significantly reduce the data distribution gap, including the gap between
negative entities and other entities, as well as the gap between negative
relations and other relations. In addition, we make the first attempt at
combining entity type and entity distance as global features, which has proven
effective, especially for the relation extraction. Experimental results on
several datasets demonstrate that the spanbased joint extraction model
augmented with the two-phase paradigm and the global features consistently
outperforms previous state-of-the-art span-based models for the joint
extraction task, establishing a new standard benchmark. Qualitative and
quantitative analyses further validate the effectiveness the proposed paradigm
and the global features.
