Levenshtein Distance

Focusing on the challenges of noise and errors in database record linkage and natural language processing, this thesis explores the application of modified Levenshtein edit distances. It enhances the WEKA API for machine learning to achieve high precision and recall in linking records of the same entity. Additionally, it presents an implementation of an approximate Gazetteer for GATE, aimed at effectively searching and annotating specified words in natural language texts.