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...
Twitter summarizes the great deal of messages posted by users in the form of trending topics that reflect the top conversations being discussed at a given moment. These trending ...
We argue that in general, the analysis of lexical cohesion factors in a document can drive a summarizer, as well as enable other content characterization tasks. More narrowly, thi...
In this paper we propose the multirelational topic model (MRTM) for multiple types of link modeling such as citation and coauthor links in document networks. In the citation networ...
Jia Zeng, William K. Cheung, Chun-hung Li, Jiming ...
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...