Fact collections are mostly built using\u00a0semi-supervised relation extraction techniques and wisdom of the crowds methods,\u00a0rendering them inherently noisy. In\u00a0this paper, we propose to validate the resulting\u00a0facts by leveraging global constraints\u00a0inherent in large fact collections,\u00a0observing that correct facts will tend to\u00a0match their arguments with other facts\u00a0more often than with incorrect ones. We\u00a0model this intuition as a graph-ranking\u00a0problem over a fact graph and explore\u00a0novel random walk algorithms. We\u00a0present an empirical study, over a large set\u00a0of facts extracted from a 500 million document\u00a0webcrawl, validating the model and\u00a0showing that it improves fact quality over\u00a0state-of-the-art methods.<\/p>\n","protected":false},"excerpt":{"rendered":"
Fact collections are mostly built using\u00a0semi-supervised relation extraction techniques and wisdom of the crowds methods,\u00a0rendering them inherently noisy. In\u00a0this paper, we propose to validate the resulting\u00a0facts by leveraging global constraints\u00a0inherent in large fact collections,\u00a0observing that correct facts will tend to\u00a0match their arguments with other facts\u00a0more often than with incorrect ones. 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