Chinese Relation Classification via Convolutional Neural Networks

Relation classification is an important task in natural language processing. Traditional relation classification techniques suffer from extensive use of linguistic features and external toolkits. In recent years, deep learning models that can automatically learn features from text are playing a more essential role in this area. In this paper we present a novel convolutional neural network (CNN) approach along shortest dependency paths (SDP) for Chinese relation classification. We first propose a baseline end-to-end model that only takes sentence-level features, and then improve its performance by joint use of pre-extracted linguistic features. The performance of the system is evaluated on the ACE 2005 Multilingual Training Corpus Chinese dataset. The baseline model achieved a 74.93% F-score on six general type relations and a 66.29% F-score on eighteen subtype relations, and the performance improved 10.71% and 13.60% respectively by incorporating linguistic features into the baseline system.