We compare two approaches for describing and generating bodies of rules used for natural language parsing.In today's parsers rule bodies do not exist a priori but are generated on the fly, usually with methods based on n-grams, which are one particular way of inducing probabilistic regular languages.We compare two approaches for inducing such languages.One is based on n-grams, the other on minimization of the Kullback-Leibler divergence.The inferred regular languages are used for generating bodies of rules inside a parsing procedure.We compare the two approaches along two dimensions: the quality of the probabilistic regular language they produce, and the performance of the parser they were used to build.The second approach outperforms the first one along both dimensions.