A two level probabilistic topic model
Marziea Rahimi, Morteza Zahedi, Hoda Mashayekhi · 2016
In this paper a new probabilistic topic model is introduced which can provides two levels of topics called specific and general topics. LDA model as a basic probabilistic topic model suffers from over-generalization and to the best of our knowledge this problem is unsolved. By adding another level of topics we tried to overcome this problem. The proposed model is applied to a corpus of 250 documents and 6143 unique words. The results show that the proposed model can produce more specific topics and also can produce clusters that are more similar to human-assigned categories.