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Markov Approximations: The Characterization Of Undermodeling Errors, Lei Lei
Markov Approximations: The Characterization Of Undermodeling Errors, Lei Lei
Theses and Dissertations
This thesis is concerned with characterizing the quality of Hidden Markov modeling when learning from limited data. It introduces a new perspective on different sources of errors to describe the impact of undermodeling. Our view is that modeling errors can be decomposed into two primary sources of errors: the approximation error and the estimation error. This thesis takes a first step towards exploring the approximation error of low order HMMs that best approximate the true system of a HMM. We introduce the notion minimality and show that best approximations of the true system with complexity greater or equal to the …
Surface Realization Using A Featurized Syntactic Statistical Language Model, Thomas L. Packer
Surface Realization Using A Featurized Syntactic Statistical Language Model, Thomas L. Packer
Theses and Dissertations
An important challenge in natural language surface realization is the generation of grammatical sentences from incomplete sentence plans. Realization can be broken into a two-stage process consisting of an over-generating rule-based module followed by a ranker that outputs the most probable candidate sentence based on a statistical language model. Thus far, an n-gram language model has been evaluated in this context. More sophisticated syntactic knowledge is expected to improve such a ranker. In this thesis, a new language model based on featurized functional dependency syntax was developed and evaluated. Generation accuracies and cross-entropy for the new language model did not …