In this paper, we study the problem of stochastic language modelling from the viewpoint of introducing suitable structures into the conditional probability distributions. The task of these distributions is to predict the probability of a new word by looking at (M) or even all predecessor words. The conventional approach is to limit (M) to 1 or 2 and to interpolate the resulting bigram and trigram models with a unigram model in a linear fashion. However, there are many other structures that can be used to model the probabilistic dependences between the predecessor word and the word to be predicted. The structures considered in this paper are: nonlinear interpolation as an alternative to linear interpolation; equivalence classes for word histories and single words; cache memory and word associations. For the optimal estimation of nonlinear and linear interpolation parameters, the leaving-one-out method is systematically used. For the determination of word equivalence classes in a bigram model, an automatic clustering procedure has been adapted. To capture long-distance dependences, we consider various models for word-by-word dependences; the cache model may be viewed as a special type of self-association. Experimental results are presented for two text databases, a Germany database and an English database.