Sentence Bag Graph Formulation for Biomedical Distant Supervision
  Relation Extraction
Hao Zhang Yang Liu Xiaoyan Liu Tianming Liang Gaurav Sharma Liang Xue Maozu Guo
We introduce a novel graph-based framework for alleviating key challenges in
distantly-supervised relation extraction and demonstrate its effectiveness in
the challenging and important domain of biomedical data. Specifically, we
propose a graph view of sentence bags referring to an entity pair, which
enables message-passing based aggregation of information related to the entity
pair over the sentence bag. The proposed framework alleviates the common
problem of noisy labeling in distantly supervised relation extraction and also
effectively incorporates inter-dependencies between sentences within a bag.
Extensive experiments on two large-scale biomedical relation datasets and the
widely utilized NYT dataset demonstrate that our proposed framework
significantly outperforms the state-of-the-art methods for biomedical distant
supervision relation extraction while also providing excellent performance for
relation extraction in the general text mining domain.