Analysis of Distant Supervision for Relation Extraction Dataset

Abstract

Deep learning techniques have been applied to relation extraction task, and demonstrated remarkable performances. However, the results of these approaches are difficult to interpret and are sometimes counter-intuitive. In this paper, we analyze the ontological and linguistic features of a relation extraction dataset and the pros and cons of existing methods for each feature type. This analysis result could help design an improved method for relation extraction by providing more insights into the dataset and models.