Probabilistic Topic Models

In this paper we present a minimally- supervised approach to the multi-domain ac- quisition of wide-coverage glossaries. We start from a small number of hypernymy rela- tion seeds and bootstrap …...

Probabilistic topic models are widely used to discover latent topics in document col- lections, while latent feature word vec- tors have been used to obtain high per- formance in many …...

AbstractProbabilistic topic models are important tools for indexing, summarizing, and analyzing large document collections by their themes. However, promoting end-user understanding of topics remains an open research problem. We compare …...

AbstractWe introduce a theoretical analysis of crosslingual transfer in probabilistic topic models. By formulating posterior inference through Gibbs sampling as a process of language transfer, we propose a new measure …...

AbstractTopic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models …...

AbstractTopic models are a useful analysis tool to uncover the underlying themes within document collections. The dominant approach is to use probabilistic topic models that posit a generative story, but …...

AbstractProbabilistic topic models in low data resource scenarios are faced with less reliable estimates due to sparsity of discrete word co-occurrence counts, and do not have the luxury of retraining …...

Neural topic modeling approach has been at- tracting much attention recently as it is able to leverage the advantages of both neural net- works and probabilistic topic models. Previ- ous …...