Improving Relation Classification with Multi-graph GCN

Abstract

As a basis task in the field of Natural Language Processing (NLP), relation extraction task aims to extract the relation between two entities in a text. Most existing models rely on a single semantic feature of the sentence for relation classification. In this paper, we present MGGCM model, a novel neural network to classify the relation of two entities in a sentence. Our neural architecture leverages two distinct graphs which are the dependency tree path and the relation-entity graph respectively. In this model, we integrate both semantic features and structural features to enhance the performance of relation extraction model. We encode the sentence through BiLSTM, obtain its structural features by GCN, and pay more attention to the entity information which is related to the target entity pair, and finally fuse the features to obtain the classification results. We test our model on the SemEval 2010 relation classification task, and achieve an F1-score of 85.7%, higher than competing methods in literature.