A scalable gibbs sampler for probabilistic entity linking
|A scalable gibbs sampler for probabilistic entity linking|
|Author(s)||Houlsby N., Ciaramita M.|
|Published in||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Keyword(s)||Unknown (Extra: Artificial intelligence, Computer science, Computers, Gibbs samplers, Gibbs sampling, Probabilistic approaches, Probabilistic inference, Side information, State-of-the-art performance, Topic Modeling, Wikipedia articles, Information retrieval)|
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|Browse properties · List of conference papers|
A scalable gibbs sampler for probabilistic entity linking is a 2014 conference paper written in English by Houlsby N., Ciaramita M. and published in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
Entity linking involves labeling phrases in text with their referent entities, such as Wikipedia or Freebase entries. This task is challenging 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 associated with a Wikipedia article. To deal with the large number of topics we propose a novel efficient Gibbs sampling scheme which can also incorporate 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.
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