Year: 1996
Type: preprint
Abstract: In this thesis, we investigate three problems involving the probabilistic modeling of language: smoothing n-gram models, statistical grammar induction, and bilingual sentence alignment. These three problems employ models at three different levels of language; they involve word-based, constituent-based, and sentence-based models, respectively. We describe techniques for improving the modeling of language at each of these levels, and surpass the performance of existing algorithms for each problem. We approach the three problems using three different frameworks. We relate each of these frameworks to the Bayesian paradigm, and show why each framework used was appropriate for the given problem. Finally, we show how our research addresses two central issues in probabilistic modeling: the sparse data problem and the problem of inducing hidden structure.
Source: arXiv (Cornell University)
Author: Chen, Stanley F.
Institution: Harvard University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1512277306