Exploring Fine-grained Entity Type Constraints for Distantly Supervised Relation Extraction

Distantly supervised relation extraction, which can automatically generate training data by aligning facts in the existing knowledge bases to text, has gained much attention. Previous work used conjunction features with coarse entity types consisting of only four types to train their models. Entity types are important indicators for a specific relation, for example, if the types of two entities are “PERSON” and “FILM” respectively, then there is more likely a “DirectorOf” relation between the two entities. However, the coarse entity types are not sufficient to capture the constraints of a relation between entities. In this paper, we propose a novel method to explore fine-grained entity type constraints, and we study a series of methods to integrate the constraints with the relation extracting model. Experimental results show that our methods achieve better precision/recall curves in sentential extraction with smoother curves in aggregated extraction which mean more stable models.