document: Probabilistic author-topic models for information discovery We propose a new unsupervised learning technique for extracting information from large text collections . We model documents as if they were generated by a two-stage stochastic process . Each author is represented by a probability distribution over topics , and each topic is represented as a probability distribution over words for that topic . The words in a multi-author paper are assumed to be the result of a mixture of each authors ' topic mixture . The topic-word and author-topic distributions are learned from data in an unsupervised manner using a Markov chain Monte Carlo algorithm . We apply the methodology to a large corpus of 160,000 abstracts and 85,000 authors from the well-known CiteSeer digital library , and learn a model with 300 topics . We discuss in detail the interpretation of the results discovered by the system including specific topic and author models , ranking of authors by topic and topics by author , significant trends in the computer science literature between 1990 and 2002 , parsing of abstracts by topics and authors and detection of unusual papers by specific authors . An online query interface to the model is also discussed that allows interactive exploration of author-topic models for corpora such as CiteSeer .
doc_bio_tags: O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O