  To solve the problem of redundant information and overlapping relations of
the entity and relation extraction model, we propose a joint extraction model.
This model can directly extract multiple pairs of related entities without
generating unrelated redundant information. We also propose a recurrent neural
network named Encoder-LSTM that enhances the ability of recurrent units to
model sentences. Specifically, the joint model includes three sub-modules: the
Named Entity Recognition sub-module consisted of a pre-trained language model
and an LSTM decoder layer, the Entity Pair Extraction sub-module which uses
Encoder-LSTM network to model the order relationship between related entity
pairs, and the Relation Classification sub-module including Attention
mechanism. We conducted experiments on the public datasets ADE and CoNLL04 to
evaluate the effectiveness of our model. The results show that the proposed
model achieves good performance in the task of entity and relation extraction
and can greatly reduce the amount of redundant information.
