Paper: FactRank: Random Walks on a Web of Facts

ACL ID C10-1057
Title FactRank: Random Walks on a Web of Facts
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010

Fact collections are mostly built using semi-supervised relation extraction tech- niques and wisdom of the crowds meth- ods, rendering them inherently noisy. In this paper, we propose to validate the re- sulting facts by leveraging global con- straints inherent in large fact collections, observing that correct facts will tend to match their arguments with other facts more often than with incorrect ones. We model this intuition as a graph-ranking problem over a fact graph and explore novel random walk algorithms. We present an empirical study, over a large set of facts extracted from a 500 million doc- umentwebcrawl, validatingthemodeland showing that it improves fact quality over state-of-the-art methods.