The dissertation of Andreas Stolcke is approved: University of ...

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Bayesian Learning of Probabilistic Language ModelsCopyright c 1994byAndreas Stolcke
Bayesian Learning of Probabilistic Language ModelsbyAndreas StolckeAbstractThe general topic of this thesis is the probabilistic modeling of language, in particular natural language. Inprobabilistic language modeling, one characterizes the strings of phonemes, words, etc. of a certain domainin terms of a probability distribution over all possible strings within the domain. Probabilistic languagemodeling has been applied to a wide range of problems in recent years, from the traditional uses in speechrecognition to more recent applications in biological sequence modeling.The main contribution of this thesis is a particular approach to the learning problem for probabilisticlanguage models, known as Bayesian model merging. This approach can be characterize as follows.Models are built either in batch mode or incrementally from samples, by incorporating individual¡samples into a working modelA uniform, small number of simple operators works to gradually transform an instance-based model to¡a generalized model that abstracts from the data.Instance-based parts of a model can coexist with generalized ones, depending on the degree of similarity¡among the observed samples, allowing the model to adapt to non-uniform coverage of the sample space.The generalization process is driven and controlled by a uniform, probabilistic metric: the Bayesian¡posterior probability of a model, integrating both criteria of goodness-of-fit with respect to the data anda notion of model simplicity (‘Occam’s Razor’).The Bayesian model merging framework is instantiated for three different classes of probabilisticmodels: Hidden Markov Models (HMMs), stochastic context-free grammars (SCFGs), and simple probabilisticattribute grammars (PAGs). Along with the theoretical background, various applications and casestudies are presented, including the induction of multiple-pronunciation word models for speech recognition(with HMMs), data-driven learning of syntactic structures (with SCFGs), and the learning of simplesentence-meaning associations from examples (with PAGs).Apart from language learning issues, a number of related computational problems involving probabilisticcontext-free grammars are discussed. A version of Earley’s parser is presented that solves the standardproblems associated with SCFGs efficiently, including the computation of sentence probabilities and sentenceprefix probabilities, finding most likely parses, and the estimation of grammar parameters.Finally, we describe an algorithm that computes ¢ -gram statistics from a given SCFG, based onsolving linear systems derived from the grammar. This method can be an effective tool to transform part of
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104Chapter 5Probabilistic Attribute
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CHAPTER 5. PROBABILISTIC ATTRIBUTE
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122Chapter 6Efficient parsing with
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Consider the following problem: sta
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CHAPTER 8. FUTURE DIRECTIONS 1828.2
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184BibliographyAHO, ALFRED V., RAVI
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BIBLIOGRAPHY 186DAGAN, IDO, FERNAND
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BIBLIOGRAPHY 188——, & ——. 1
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BIBLIOGRAPHY 190QUINLAN, J. ROSS, &
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BIBLIOGRAPHY 192WALLACE, C. S., & P
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