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In this paper, we describe a unified probabilistic framework for statistical language modeling-the latent maximum entropy principle-which can effectively incorporate various aspects of natural language, such as local word interaction, syntactic structure and semantic document information. Unlike previous work on maximum entropy methods for language modeling, which only allow explicit features to be modeled, our framework also allows relationships over hidden features to be captured, resultingdoi:10.1109/icassp.2003.1198796 dblp:conf/icassp/WangSPZ03 fatcat:c3qdgkeewncm5lhez76pgnoyhu