Word Co-occurrence Augmented Topic Model in Short Text

Topic models learn topics base on the amount of the word co-occurrence in the documents. The word co-occurrence is a degree which describes how often the two words appear together. BTM, discovers topics from bi-terms in the whole corpus to overcome the lack of local word co-occurrence information. However, BTM will make the common words be performed excessively because BTM identifies the word co-occurrence information by the bi-term The 2015 Conference on Computational Linguistics and Speech Processing ROCLING 2015, pp. 164-166  The Association for Computational Linguistics and Chinese Language Processing