  We present an LDA approach to entity disambiguation. Each topic is associated
with a Wikipedia article and topics generate either content words or entity
mentions. Training such models is challenging because of the topic and
vocabulary size, both in the millions. We tackle these problems using a novel
distributed inference and representation framework based on a parallel Gibbs
sampler guided by the Wikipedia link graph, and pipelines of MapReduce allowing
fast and memory-frugal processing of large datasets. We report state-of-the-art
performance on a public dataset.
