Relation extraction based on two-step classification with distant supervision

Supervised machine learning methods have been widely used in relation extraction to find the relation between two named entities in a sentence. However, the disadvantages of supervised machine learning methods are that constructing the training data set is costly and time-consuming, and the machine learning system is ultimately dependent on the specific domain of the training data. To overcome these disadvantages, we propose a two-step relation extraction model with distant supervision. The two-step model consists of a one-class model and a multi-class model. The one-class model selects positive sentences from input sentences and the multi-class model classifies the positive sentences into specific classes. In the experiments, the proposed model showed good F1-measures (62.9 % in the auto-labeled test data, 63.8 % in the gold-labeled test data), although it does not use any human-labeled training data.