The paper introduces a maximum entropy approach to statistical modeling, particularly in the context of natural language processing (NLP). The authors trace the historical roots of maximum entropy to Biblical times and highlight its recent application in statistical estimation and pattern recognition. The paper outlines a method for constructing maximum entropy models using maximum likelihood estimation and discusses efficient implementation techniques. It also addresses feature selection and presents an automatic method for discovering facts about a process from a sample of output data. The paper applies these methods to several NLP tasks, including bilingual sense disambiguation, word reordering, and sentence segmentation. The authors demonstrate the effectiveness of the maximum entropy approach through case studies in a French-to-English machine translation system, highlighting its ability to incorporate context-dependent information and improve translation accuracy.The paper introduces a maximum entropy approach to statistical modeling, particularly in the context of natural language processing (NLP). The authors trace the historical roots of maximum entropy to Biblical times and highlight its recent application in statistical estimation and pattern recognition. The paper outlines a method for constructing maximum entropy models using maximum likelihood estimation and discusses efficient implementation techniques. It also addresses feature selection and presents an automatic method for discovering facts about a process from a sample of output data. The paper applies these methods to several NLP tasks, including bilingual sense disambiguation, word reordering, and sentence segmentation. The authors demonstrate the effectiveness of the maximum entropy approach through case studies in a French-to-English machine translation system, highlighting its ability to incorporate context-dependent information and improve translation accuracy.