Effective Selectional Restrictions for Unsupervised Relation Extraction

Unsupervised Relation Extraction (URE) methods automatically discover semantic relations in text corpora of unknown content and extract for each discovered relation a set of relation instances. Due to the sparsity of the feature space, URE is vulnerable to ambiguities and underspecification in patterns. In this paper, we propose to increase the discriminative power of patterns in URE using selectional restrictions (SR). We propose a method that utilizes a Web-derived soft clustering of n-grams to model selectional restrictions in the open domain. We comparatively evaluate our method against a baseline without SR, a setup in which standard 7class Named Entity types are used as SR and a setup that models SR using a finegrained entity type system. Our results indicate that modeling SR into patterns significantly improves the ability of URE to discover relations and enables the discovery of more fine-granular relations.