Latent Dirichlet allocation (LDA) and other related topic models are increasingly popular tools for summarization and manifold discovery in discrete data. However, LDA does not ca...
—Knowledge discovery from scientific articles has received increasing attentions recently since huge repositories are made available by the development of the Internet and digit...
This paper concerns learning and prediction with probabilistic models where the domain sizes of latent variables have no a priori upper-bound. Current approaches represent prior d...
Statistical topic models such as the Latent Dirichlet Allocation (LDA) have emerged as an attractive framework to model, visualize and summarize large document collections in a co...
Ramesh Nallapati, Amr Ahmed, William W. Cohen, Eri...
The aim of query-based sampling is to obtain a sufficient, representative sample of an underlying (text) collection. Current measures for assessing sample quality are too coarse gr...