  Relation extraction is the problem of classifying the relationship between
two entities in a given sentence. Distant Supervision (DS) is a popular
technique for developing relation extractors starting with limited supervision.
We note that most of the sentences in the distant supervision relation
extraction setting are very long and may benefit from word attention for better
sentence representation. Our contributions in this paper are threefold.
Firstly, we propose two novel word attention models for distantly- supervised
relation extraction: (1) a Bi-directional Gated Recurrent Unit (Bi-GRU) based
word attention model (BGWA), (2) an entity-centric attention model (EA), and
(3) a combination model which combines multiple complementary models using
weighted voting method for improved relation extraction. Secondly, we introduce
GDS, a new distant supervision dataset for relation extraction. GDS removes
test data noise present in all previous distant- supervision benchmark
datasets, making credible automatic evaluation possible. Thirdly, through
extensive experiments on multiple real-world datasets, we demonstrate the
effectiveness of the proposed methods.
