  As one of the prevalent topic mining tools, neural topic modeling has
attracted a lot of interests for the advantages of high efficiency in training
and strong generalisation abilities. However, due to the lack of context in
each short text, the existing neural topic models may suffer from feature
sparsity on such documents. To alleviate this issue, we propose a Context
Reinforced Neural Topic Model (CRNTM), whose characteristics can be summarized
as follows. Firstly, by assuming that each short text covers only a few salient
topics, CRNTM infers the topic for each word in a narrow range. Secondly, our
model exploits pre-trained word embeddings by treating topics as multivariate
Gaussian distributions or Gaussian mixture distributions in the embedding
space. Extensive experiments on two benchmark datasets validate the
effectiveness of the proposed model on both topic discovery and text
classification.
