Topic Modeling Using Distributed Word Embeddings
Table 5: Details of all corpora studied
['Corpus', 'Documents', 'Tokens', '| [ITALIC] V|', 'Runtime']
In all our experiments, the algorithm does not take more than a few minutes to run. The slowest component of Vec2Topic is building word vectors, which took majority of the run time. However, this method is quite scalable and has been used to build word vectors on a Google news dataset of 100 billion tokens in a couple of days. Indeed, we trained our knowledge-base vectors on Wikipedia corpus, which contains about 6 billion tokens and about 2.8 million unique words in about 6 hours.