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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 modeling. The main contribution of this thesis is a particular approach to the learning problem for probabilistic language models, known as Bayesian model merging. This approach can be characterize as follows. ffl Models are built either in batch mode or incrementally from samples, by incorporating individual samples into a working model ffl A uniform, small number of simple operators works to gradually transform an instance-based model to a generalized model that abstracts from the data. ffl 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. ffl The generalization process is driven and controlled by a uniform, probabilistic metric: the Bayesian, |
