End-to-end relation extraction based on part of speech syntax tree

Recently, deep learning has achieved richer research results on entity relationship extraction tasks. Existing methods mainly focus on the character features of the sequence input, without considering that the input structural features may learn meaningless subsequences. In this paper, we propose an end-to-end joint extraction model based on syntactic tree structure, which can learn character features and sentence structure features at the same time. In the decoding process, we use a tree structure to learn sentence structure features based on character parts of speech. We test our models in two public datasets and our model outperforms the baseline method significantly.