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Bayesian Learning of Probabilistic Language Models (1994)  (Make Corrections)  (57 citations)
Andreas Stolcke



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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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