We present an algorithm for computing n-gram probabilities from stochastic context-free grammars, a procedure that can alleviate some of the standard problems associated with n-grams (estimation from sparse data, lack of linguistic structure, among others). The method operates via the computation of substring expectations, which in turn is accomplished by solving systems of linear equations derived from the grammar. The procedure is fully implemented and has proved viable and useful in practice. confirming its practical feasibility and utility. The technique of compiling higher-level grammatical models into lower-level ones has precedents: Zue et al. (1991) report building a word-pair grammar.
