  This paper proposes the continuous semantic topic embedding model (CSTEM)
which finds latent topic variables in documents using continuous semantic
distance function between the topics and the words by means of the variational
autoencoder(VAE). The semantic distance could be represented by any symmetric
bell-shaped geometric distance function on the Euclidean space, for which the
Mahalanobis distance is used in this paper. In order for the semantic distance
to perform more properly, we newly introduce an additional model parameter for
each word to take out the global factor from this distance indicating how
likely it occurs regardless of its topic. It certainly improves the problem
that the Gaussian distribution which is used in previous topic model with
continuous word embedding could not explain the semantic relation correctly and
helps to obtain the higher topic coherence. Through the experiments with the
dataset of 20 Newsgroup, NIPS papers and CNN/Dailymail corpus, the performance
of the recent state-of-the-art models is accomplished by our model as well as
generating topic embedding vectors which makes possible to observe where the
topic vectors are embedded with the word vectors in the real Euclidean space
and how the topics are related each other semantically.
