Algorithms that enable the process of automatically mining distinct topics in document collections have become increasingly important due to their applications in many fields and ...
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...
We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian m...
Topic-based text summaries promise to help average users quickly understand a text collection and derive insights. Recent research has shown that the Latent Dirichlet Allocation (...
Models of latent document semantics such as the mixture of multinomials model and Latent Dirichlet Allocation have received substantial attention for their ability to discover top...
Daniel David Walker, William B. Lund, Eric K. Ring...