Joint extraction of entities and relations using graph convolution over pruned dependency trees

摘要

We present a novel end-to-end deep neural network model based on graph convolutional networks for simultaneous joint extraction of entities and relations among them. Our model captures context and syntactic information from sentence by stacking a graph convolutional layer over bidirectional sequential LSTM layers. We sequentially concatenate the subject, object and sentence representations for obtaining the directionality of relations. Besides, in order to address long entity-distances problem, we further apply a path-centric pruning procedure to input trees in order to preserve useful information while maximally removing irrelevant words. Experiments are conducted on NYT dataset, and the proposed model achieves the state-of-the-art results on entity and relation extraction task. Our source code is available on Github: https://github.com/michael-hon/LSTM-GCN-ER. (c) 2020 Elsevier B.V. All rights reserved.