Branch-combined PLSA for Topic Extraction
Jiali Lin, Zhiqiang Wei, Zhen Li · International Journal of Database Theory and Application · 2017
With the developing of the Internet technology, the information on the network is expanding at the speed of geometric progression. Facing such vast network information, quickly extracting the important information becomes the urgent needs. The subject extraction model is a good solution to the problem. In this paper, a new model based on Probabilistic Latent Semantic Analysis (PLSA) is proposed which is called Branchcombined PLSA (BPLSA). BPLSA divides training data into two subsets, and trains subsets separately first, then the global training is implemented.At the same time, Message Passing Interface (MPI) is used for parallel computing to speed up the proposed method.Through the parallelization of the BPLSA, the efficiency is improved greatly.