Topic models have been applied to everything from books to newspapers to social media
posts in an effort to identify the most prevalent themes of a text corpus. We provide an 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 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 …

Due to the inception of Web 2.0 and freedom to facilitate the dissemination of information,
sharing views, expressing opinions with regards to current world level events, services …

Recent work incorporates pre-trained word embeddings such as BERT embeddings into
Neural Topic Models (NTMs), generating highly coherent topics. However, with high-quality …

Recent empirical studies show that adversarial topic models (ATM) can successfully capture
semantic patterns of the document by differentiating a document with another dissimilar …

Topic models have been prevalent for decades with various applications. However, existing
topic models commonly suffer from the notorious topic collapsing: discovered topics …

Topic model evaluation, like evaluation of other unsupervised methods, can be contentious.
However, the field has coalesced around automated estimates of topic coherence, which …

Topic models have been prevailing for many years on discovering latent semantics while
modeling long documents. However, for short texts they generally suffer from data sparsity …

To overcome the data sparsity issue in short text topic modeling, existing methods commonly
rely on data augmentation or the data characteristic of short texts to introduce more word co …