We train the LDA model and the topic-specific word embeddings on the British National Corpus (BNC) (Consortium and others, 2007), which contains more than 93 million terms, and a bigger corpus -a snapshot of the English Wikipedia corpus 1 with about 990 million tokens.
we use GibbsLDA++ (Phan and Nguyen, 2007) to estimate the topic distribution and infer the topic assignment for each word in the corpus.