The need to correct garbled strings arises in many areas of natural language processing. If a dictionary is available that covers all possible input tokens, a natural set of candidates for correcting an erroneous input P is the set of all words in the dictionary for which the Levenshtein distance to P does not exceed a given (small) bound k. In this article we describe methods for efficiently s...

Dictionary lookup methods are popular in dealing with ambiguous letters which were not recognized by Optical Character Readers. However, a robust dictionary lookup method can be complex as apriori probability calculation or a large dictionary size increases the overhead and the cost of searching. In this context, Levenshtein distance is a simple metric which can be an effective string approxima...

This paper describes a framework to extract the effective correction rules from the sentence-aligned corpus and show a practical application: auto-editing using the found rules. The framework exploits the methodology of finding Levenshtein distance between sentences to identify the key parts of the rules and then use the editing corpus to filter, condense and refine the rules. We produce the ru...

This paper presents a methodology for calculating a modified Levenshtein edit distance between character strings, and applies it to the task of automated cognate identification from nonparallel (comparable) corpora. This task is an important stage in developing MT systems and bilingual dictionaries beyond the coverage of traditionally used aligned parallel corpora, which can be used for finding...