Abstract
This thesis studies graphical Models applied to the correction of words. More particularly, hidden Markov models and Markov chains will be used in order to build a probabilistic spellchecker. Several ways to cluster words will be introduced: the batchK-Means clustering algorithm with a specific distance measure and the Expectation- Maximization algorithm in order to learn a mixture of Markov chains. Moreover, a
solution for dealing with the suffixes and prefixes will be presented.
Date of Award | Sept 2000 |
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Original language | English |
Awarding Institution |
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Keywords
- probabilistic
- spell checker
- information engineering