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Abstract: The general topic of this thesis is the probabilistic modeling of language, in particular natural language. In
probabilistic language modeling, one characterizes the strings of phonemes, words, etc. of a certain domain
in terms of a probability distribution over all possible strings within the domain. Probabilistic language
modeling has been applied to a wide range of problems in recent years, from the traditional uses in speech
recognition to more recent applications in biological sequence... (Update)
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BibTeX entry: (Update)
A. Stolcke. Bayesian Learning of Probabilistic Language Models. Dissertation, U. California, Berkeley, 1994. http://citeseer.ist.psu.edu/article/stolcke94bayesian.html More
@phdthesis{ stolcke94bayesian,
author = "Andreas Stolcke",
title = "Bayesian Learning of Probabilistic Language Models",
address = "Berkeley, CA",
year = "1994",
url = "citeseer.ist.psu.edu/article/stolcke94bayesian.html" }
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