Joint extraction of entities and relations by a novel end-to-end model with a double-pointer module

Joint extraction of entities and relations is to detect entities and recognize semantic relations simultaneously. However, some existing joint models predict relations on words, instead of entities. These models cannot make full use of the entity information when predicting relations, which will affect relation extraction. We propose an end-to-end model with a double-pointer module that can jointly extract whole entities and relations. The double-pointer module is combined with multiple decoders to predict the start and end positions of the entity in the input sentence. In addition, in order to learn the relevance between long-distance entities effectively, the multi-layer convolution and self-attention mechanism are used as an encoder, instead of Bi-RNN. We conduct experiments on two public datasets and our models outperform the baseline methods significantly.