Topic modelling is a popular task of natural language processing (NLP) aimed to
automatically discover the main, shared topics of a given collection of documents. In …

Topic models have been prevalent for decades to discover latent topics and infer topic
proportions of documents in an unsupervised fashion. They have been widely used in …

Topic modeling techniques have the benefits of modeling words and documents uniformly
under a probabilistic framework. However, they also suffer from the limitations of sensitivity …

Topic modelling is an important approach of unsupervised machine learning that allows
automatically extracting the main “topics” from large collections of documents. In addition …

Neural topic modeling approach has been attracting much attention recently as it is able to
leverage the advantages of both neural networks and probabilistic topic models. Previous …

Topic models have been thoroughly investigated for multiple years due to their great
potential in analyzing and understanding texts. Recently, researchers combine the study of …

Topic modeling is an unsupervised natural language processing approach for automatically
extracting the main topics from a large collection of documents, and simultaneously …

From statistical to neural models, a wide variety of topic modelling algorithms have been
proposed in the literature. However, because of the diversity of datasets and metrics, there …

Topic modelling has been a successful technique for text analysis for almost twenty years.
When topic modelling met deep neural networks, there emerged a new and increasingly …

Topic models have been widely used to learn text representations and gain insight into
document corpora. To perform topic discovery, most existing neural models either take …