Proceedings of the Annual Meeting of the Cognitive Science Society

Dependency tree is efficient for relation extraction model to exploit relations between words.Recent approaches have achieved promising performance while still suffering from inherent limitations, such as the computation efficiency and flexible pruning strategies. In this paper, we propose a novel relation extraction framework called Progressive Graph Learning over pruned dependency tree (PGLNet). PGLNet constructs a set of graphs by progressively adapting to input sentence. Specially, we implement the model to construct progressive weighted adjacency matrices by learning the relations among graph nodes with multi-head self-attention mechanism.Then, the model takes the learned weights as reference to prune dependency tree in order to preserve useful relevant sub-structures for the relation extraction while removing irrelevant words. Next, progressive convolution module is designed to encode the relations of entities and followed by relations classification.We evaluate our proposed model using public real-world datasets, experimental results demonstrate that the proposed model achieves state-of-the-art performance with consistency in all datasets. We conclude that the ability of PGLNet to progressively adapt to input data and enable the model with robustness.