Exploratory neural relation classification for domain knowledge acquisition

The state-of-the-art methods for relation classification are primarily based on deep neural networks. This supervised learning method suffers from not only limited training data, but also the large number of low-frequency relations in specific domains. In this paper, we propose an exploratory relation classification method for domain knowledge harvesting. The goal is to learn a classifier on pre-defined relations while discovering new relations expressed in texts. A dynamically structured neural network is introduced to classify entity pairs to a continuously expanded relation set. We further propose the similarity sensitive Chinese restaurant process to discover new relations. Experiments conducted on a large corpus show that new relations are discovered with high precision and recall, illustrating the effectiveness of our method.