Existing relation classification methods that rely on distant supervision
assume that a bag of sentences mentioning an entity pair are all describing a
relation for the entity pair. Such methods, performing classification at the
bag level, cannot identify the mapping between a relation and a sentence, and
largely suffers from the noisy labeling problem. In this paper, we propose a
novel model for relation classification at the sentence level from noisy data.
The model has two modules: an instance selector and a relation classifier. The
instance selector chooses high-quality sentences with reinforcement learning
and feeds the selected sentences into the relation classifier, and the relation
classifier makes sentence level prediction and provides rewards to the instance
selector. The two modules are trained jointly to optimize the instance
selection and relation classification processes. Experiment results show that
our model can deal with the noise of data effectively and obtains better
performance for relation classification at the sentence level.