Refine bigram PLSA model by assigning latent topics unevenly

As an important component in many speech and language processing applications, statistical language model has been widely investigated. The bigram topic model, which combines advantages of both the traditional n-gram model and the topic model, turns out to be a promising language modeling approach. However, the original bigram topic model assigns the same topic number for each context word but ignores the fact that there are different complexities to the latent semantics of context words, we present a new bigram topic model, the bigram PLSA model, and propose a modified training strategy that unevenly assigns latent topics to context words according to an estimation of their latent semantic complexities. As a consequence, a refined bigram PLSA model is reached. Experiments on HUB4 Mandarin test transcriptions reveal the superiority over existing models and further performance improvements on perplexity are achieved through the use of the refined bigram PLSA model.