Towards Large-Scale Unsupervised Relation Extraction from the Web
The Web brings an open-ended set of semantic relations. Discovering the significant types is very challenging. Unsupervised algorithms have been developed to extract relations from a corpus without knowing the relation types in advance, but most rely on tagging arguments of predefined types. One recently reported system is able to jointly extract relations and their argument semantic classes, taking a set of relation instances extracted by an open IE (Information Extraction) algorithm as input. However, it cannot handle polysemy of relation phrases and fails to group many similar ("synonymous") relation instances because of the sparseness of features. In this paper, the authors present a novel unsupervised algorithm that provides a more general treatment of the polysemy and synonymy problems. The algorithm incorporates various knowledge sources which they will show to be very effective for unsupervised relation extraction. Moreover, it explicitly disambiguates polysemous relation phrases and groups synonymous ones. While maintaining approximately the same precision, the algorithm achieves significant improvement on recall compared to the previous method. It is also very efficient. Experiments on a real-world dataset show that it can handle 14.7 million relation instances and extract a very large set of relations from the Web.
large-scale relation extraction web semantics information extraction unsupervised learning