Modeling the evolution of topics with time is of great value in automatic summarization and analysis of large document collections. In this work, we propose a new probabilistic gr...
Ramesh Nallapati, Susan Ditmore, John D. Lafferty,...
Online social networking sites such as Flickr and Facebook provide a diverse range of functionalities that foster online communities to create and share media content. In particul...
Yu-Ru Lin, Hari Sundaram, Munmun De Choudhury, Ais...
The growing amount of online news posted on the WWW demands new algorithms that support topic detection, search, and navigation of news documents. This work presents an algorithm f...
We propose a generative model based on latent Dirichlet allocation for mining distinct topics in document collections by integrating the temporal ordering of documents into the ge...
Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee G...
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the n...