  Relation classification is an important NLP task to extract relations between
entities. The state-of-the-art methods for relation classification are
primarily based on Convolutional or Recurrent Neural Networks. Recently, the
pre-trained BERT model achieves very successful results in many NLP
classification / sequence labeling tasks. Relation classification differs from
those tasks in that it relies on information of both the sentence and the two
target entities. In this paper, we propose a model that both leverages the
pre-trained BERT language model and incorporates information from the target
entities to tackle the relation classification task. We locate the target
entities and transfer the information through the pre-trained architecture and
incorporate the corresponding encoding of the two entities. We achieve
significant improvement over the state-of-the-art method on the SemEval-2010
task 8 relational dataset.
