A Scalable Gibbs Sampler for Probabilistic Entity Linking

Abstract. Entity linking involves labeling phrases in text with their referent entities, such as Wikipedia or Freebase entries. This task is chal-lenging due to the large number of possible entities, in the millions, and heavy-tailed mention ambiguity. We formulate the problem in terms of probabilistic inference within a topic model, where each topic is associ-ated with a Wikipedia article. To deal with the large number of topics we propose a novel efficient Gibbs sampling scheme which can also incor-porate side information, such as the Wikipedia graph. This conceptually simple probabilistic approach achieves state-of-the-art performance in entity-linking on the Aida-CoNLL dataset.