Weighted graph convolution over dependency trees for nontaxonomic relation extraction on public opinion information

Currently, with the continuous development of relation extraction tasks, we notice that the ability to extract nontaxonomic relations has improved frustratingly slowly, and the only relation extraction dataset in the field of public opinion is the New York Times dataset (NYT) annotated by distant supervision. This paper simultaneously addresses two issues. We first propose a new model that is tailored for nontaxonomic relation extraction, which combines a context-aware model with a weighted graph convolutional network (WGCN) model characterized by dependency trees. It effectively blends contextual and dependent structural information. We further apply a pruning strategy to the input tree so that the model can effectively retain valid information and delete redundant information. Then, we build a supervised Chinese relation extraction dataset, XUNRED (Xinjiang University Nontaxonomic Relation Extraction Dataset), which is obtained after manually tagging the Baidu Encyclopedia, Baidu Post Bar and Baidu Information Flow text, and address the nontaxonomic relation in the public opinion domain. The experimental results on this new dataset show that our model can combine the contextual information with the structural information in the dependency tree better than other models.