A convex relaxation for weakly supervised relation extraction
Édouard Grave · 2014
A promising approach to relation extrac-tion, called weak or distant supervision, exploits an existing database of facts as training data, by aligning it to an unla-beled collection of text documents. Using this approach, the task of relation extrac-tion can easily be scaled to hundreds of different relationships. However, distant supervision leads to a challenging multi-ple instance, multiple label learning prob-lem. Most of the proposed solutions to this problem are based on non-convex formu-lations, and are thus prone to local min-ima. In this article, we propose a new approach to the problem of weakly su-pervised relation extraction, based on dis-criminative clustering and leading to a convex formulation. We demonstrate that our approach outperforms state-of-the-art methods on the challenging dataset intro-duced by Riedel et al. (2010). 1