A Survey on Neural Topic Models: Methods, Applications, and Challenges
Xiaobao Wu Thong Nguyen Anh Tuan Luu
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 various applications like text analysis and context
recommendation. Recently, the rise of neural networks has facilitated the
emergence of a new research field -- Neural Topic Models (NTMs). Different from
conventional topic models, NTMs directly optimize parameters without requiring
model-specific derivations. This endows NTMs with better scalability and
flexibility, resulting in significant research attention and plentiful new
methods and applications. In this paper, we present a comprehensive survey on
neural topic models concerning methods, applications, and challenges.
Specifically, we systematically organize current NTM methods according to their
network structures and introduce the NTMs for various scenarios like short
texts and cross-lingual documents. We also discuss a wide range of popular
applications built on NTMs. Finally, we highlight the challenges confronted by
NTMs to inspire future research.