Relevance-based language models operate by estimating the probabilities of observing words in documents relevant (or pseudo relevant) to a topic. However, these models assume that ...
We introduce supervised latent Dirichlet allocation (sLDA), a statistical model of labelled documents. The model accommodates a variety of response types. We derive a maximum-like...
The latent topic model plays an important role in the unsupervised learning from a corpus, which provides a probabilistic interpretation of the corpus in terms of the latent topic...
This paper introduces a novel probabilistic activity modeling approach that mines recurrent sequential patterns from documents given as word-time occurrences. In this model, docum...
Some models of textual corpora employ text generation methods involving n-gram statistics, while others use latent topic variables inferred using the "bag-of-words" assu...