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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 generatingdoi:10.3115/1218955.1219013 dblp:conf/acl/LopezR04 fatcat:psnodjlos5dgbfc7eu3vqxwhmi