  Topic models have been widely used in discovering latent topics which are
shared across documents in text mining. Vector representations, word embeddings
and topic embeddings, map words and topics into a low-dimensional and dense
real-value vector space, which have obtained high performance in NLP tasks.
However, most of the existing models assume the result trained by one of them
are perfect correct and used as prior knowledge for improving the other model.
Some other models use the information trained from external large corpus to
help improving smaller corpus. In this paper, we aim to build such an algorithm
framework that makes topic models and vector representations mutually improve
each other within the same corpus. An EM-style algorithm framework is employed
to iteratively optimize both topic model and vector representations.
Experimental results show that our model outperforms state-of-art methods on
various NLP tasks.
