  Topic modeling has been one of the most active research areas in machine
learning in recent years. Hierarchical latent tree analysis (HLTA) has been
recently proposed for hierarchical topic modeling and has shown superior
performance over state-of-the-art methods. However, the models used in HLTA
have a tree structure and cannot represent the different meanings of multiword
expressions sharing the same word appropriately. Therefore, we propose a method
for extracting and selecting collocations as a preprocessing step for HLTA. The
selected collocations are replaced with single tokens in the bag-of-words model
before running HLTA. Our empirical evaluation shows that the proposed method
led to better performance of HLTA on three of the four data sets tested.
