Multi-information interaction graph neural network for joint entity and relation extraction

Overlap situation where different triplets share entities or relations is a common challenge in joint entity and relation extraction task. On the one hand, there is strong correlation between overlapping triplets. On the other hand, most of the existing large-scale training data come from distant supervision, which introduces incomplete annotations. These practical problems make the information interaction between triplets particularly important. However, there are two problems with the existing methods: (i) the neglect of information interaction between different triplets; (ii) the limited information utilization caused by the specific decoding order. To solve the above problems, we decompose decoding and information interaction. Specifically, entity and relation proposals are obtained by a proposal generator, then a multi-information interaction graph neural network with parallel decoder is proposed to complete the joint extraction task. In this way, the inherent decoding order is broken to achieve the purpose of fully exploiting multi-information interaction across triplets and within triplets. Experimental results show that our proposed model outperforms previous work, especially in the case of incomplete annotations. •Limitations introduced by quality and order of decoding are addressed.•More comprehensive information interaction is proposed for overlap triplets.•Excellent results are achieved under the incomplete annotation protocol.