Distant Supervision for Relation Extraction via Piecewise Attention and Bag-Level Contextual Inference

Distant supervision (DS) has become an efficient approach for relation extraction (RE) to alleviate the lack of labeled examples in supervised learning. In this paper, we propose a novel neural RE model that combines a bidirectional gated recurrent unit model with a form of hierarchical attention that is better suited to RE. We demonstrate that an additional attention mechanism called piecewise attention, which builds itself upon segment level representations, significantly enhances the performance of the distantly supervised relation extraction task. Our piecewise attention mechanism not only captures crucial segments in each sentence but also reflects the direction of relations between two entities. Furthermore, we propose a contextual inference method that can infer the most likely positive examples of an entity pair in bags with very limited contextual information. In addition, we provide an annotated dataset without false positive examples based on the Riedel testing dataset, and report on the actual performance of several RE models. The experimental results show that our proposed methods outperform the previous state-of-the-art baselines on both original and annotated datasets for the distantly supervised RE task.