Incorporating bidirectional interactive information and regional features for relational facts extraction

Extracting entity and relation jointly is often complicated since the relational triplets may be overlapped. In this paper, we propose a novel unified joint extraction model that considers the significant information which is useful for relation extraction between a pair of entities. We also consider bidirectional interaction between named entity recognition and relation extraction. To this end, we apply Bi-LSTM to capture sequential information and use Graph Convolutional Network to capture significant regional information in our encoding part. We use multi-layer structure in decoding part including first decode layer, interactive layer and final decode layer to fuse bidirectional interactive information between named entity recognition and relation extraction. In this way, our method can simultaneously extract all entities and their relations including overlapping relations. Experimental results show that our model performs better comparing with other baseline models in this task, and we achieve state-of-the-art performance on two public datasets.