Topic models are learned via a statistical model of variation within document collections, but
designed to extract meaningful semantic structure. Desirable traits include the ability to …

In this paper we propose guaranteed spectral methods for learning a broad range of topic
models, which generalize the popular Latent Dirichlet Allocation (LDA). We overcome the …

We propose a Bayesian nonparametric topic model that rep-resents relationships between
given labels and the corre-sponding words/phrases, from supervised articles. Unlike existing …

Topic models infer latent topic distributions based on observed word co-occurrences in a
text corpus. While typically a corpus contains documents of variable lengths, most previous …

We address two challenges in topic models:(1) Context information around words helps in
determining their actual meaning, eg,“networks” used in the contexts artificial neural …

Topic modeling has emerged as a dominant method for exploring large document
collections. Recent approaches to topic modeling use large contextualized language …

We show how to learn a neural topic model with discrete random variables---one that
explicitly models each word's assigned topic---using neural variational inference that does …

Topic modeling is a popular method for discovering semantic information from textual data,
with latent Dirichlet allocation (LDA) being a representative model. Recently, researchers …

Topic models have been widely explored as probabilistic generative models of documents.
Traditional inference methods have sought closed-form derivations for updating the models …

Topic models such as Latent Dirichlet Allocation (LDA) and Correlated Topic Model (CTM)
have recently emerged as powerful statistical tools for text document modeling. In this paper …