Statistical topic models provide a general data-driven framework for automated discovery of high-level knowledge from large collections of text documents. While topic models can p...
Chaitanya Chemudugunta, Padhraic Smyth, Mark Steyv...
The four-level pachinko allocation model (PAM) (Li & McCallum, 2006) represents correlations among topics using a DAG structure. It does not, however, represent a nested hiera...
Automatic generation of taxonomies can be useful for a wide area of applications. In our application scenario a topical hierarchy should be constructed reasonably fast from a large...
clustering of documents according to sharing of topics at multiple levels of abstraction. Given a corpus of documents, a posterior inference algorithm finds an approximation to a ...
David M. Blei, Thomas L. Griffiths, Michael I. Jor...
Statistical language models can learn relationships between topics discussed in a document collection and persons, organizations and places mentioned in each document. We present a...
David Newman, Chaitanya Chemudugunta, Padhraic Smy...