−4.37441 −6.88172 −10.6397 −0.141938 −3.65821 −8.81816 −3.60823 −4.29886 −7.64586 c. −3.57338 −6.86285 −10.9656 −0.131813 −3.8662 −8.85551 −3.42688 −4.28615 −7.82392 d. −4.61674 −6.49064 −13.0595 −0.255452 −3.73302 −5.55244 −3.60481 −3.97708 −7.85289 e. −3.85647 −6.61406 −12.5666 −0.178075 −3.92356 −5.90511 −3.46416 −4.03158 −7 The conditional probability of the word(s) party or political bureau given document history computed by 5-gram/PLSA or 5-gram/PLSA+4-SLM/PLSA is significantly boosted due to the appearance of semantic related words such as cpc and communist party in the previous sentences, this clearly shows that the composite language models (5-gram/PLSA and 5-gram/PLSA+4-SLM/PLSA) trigger long-span document-level discourse topics to influence word prediction.
there is no effect when using linear combination models (i.e., 5-gram+PLSA and 5-gram+4-SLM+PLSA).