Abstract and Keywords
Statistical methods now belong to mainstream natural language processing. They have been successfully applied to virtually all tasks within language processing and neighbouring fields, including part-of-speech tagging, syntactic parsing, semantic interpretation, lexical acquisition, machine translation, information retrieval, and information extraction and language learning. This article reviews mathematical statistics and applies it to language modelling problems, leading up to the hidden Markov model and maximum entropy model. The real strength of maximum-entropy modelling lies in combining evidence from several rules, each one of which alone might not be conclusive, but which taken together dramatically affect the probability. Maximum-entropy modelling allows combining heterogeneous information sources to produce a uniform probabilistic model where each piece of information is formulated as a feature. The key ideas of mathematical statistics are simple and intuitive, but tend to be buried in a sea of mathematical technicalities. Finally, the article provides mathematical detail related to the topic of discussion.
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