Bayesian learning of probabilistic language models
Andreas Stolcke · 1994
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. • Models are built either in batch mode or incrementally from samples, by incorporating individual samples into a working model • A uniform, small number of simple operators works to gradually transform an instance-based model to a generalized model that abstracts from the data. • Ins...