All composite language models are first trained by performing the N-best list approximate EM algorithm until convergence, then the EM algorithm for a second stage of parameter re-estimation for WORD-PREDICTOR and SEMANTIZER until convergence. We fix the size of topics in the PLSA to be 200 and then prune to 5 in the experiments, where the unpruned 5 topics in general account for 70% probability in p(g|d). Table 5 shows comprehensive perplexity results for a variety of different models such as composite n-gram/m-SLM, n-gram/PLSA, m-SLM/PLSA, their linear combinations, and so on, where we use on-line EM with a fixed learning rate to re-estimate the parameters of the SEMANTIZER of test document. The m-SLM performs competitively with its counterpart n-gram (n = m + 1) on large scale corpus. Table 6 lists the statistics about the number of types in the predictor of the m-SLMs on these three corpora, where for the 230 million token and 1.3 billion token corpora we cut off the fractional expected counts that are less than a predefined threshold of 0.005, to significantly reduce the number of the predictor's types by 70%.