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...
Text data pertaining to socio-technical networks often are analyzed separately from relational data, or are reduced to the fact and strength of the flow of information between node...
This paper considers extractive summarization of Chinese spoken documents. In contrast to conventional approaches, we attempt to deal with the extractive summarization problem und...
In this work, we address the problem of joint modeling of text and citations in the topic modeling framework. We present two different models called the Pairwise-Link-LDA and the ...
Ramesh Nallapati, Amr Ahmed, Eric P. Xing, William...
Topic models provide a powerful tool for analyzing large text collections by representing high dimensional data in a low dimensional subspace. Fitting a topic model given a set of...