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This is a review of [[reviewed paper::Cohen_2003_a_comparison_of_string_distance_metrics_for_name_matching_tasks]] by [[reviewer::user:Apappu]]. Edit distance like Levenshtein -- Dynamic programming based on unit cost Monger-Elkan --- a variant of edit distance common characters: Number and order based Jaro metric -- makes note of common subsequence Jaro-Winkler -- uses the length of P of the longest common prefix Token based distance functions: jaccard similarity, TFIDF, then Jennson Shannon distance based on unknown distributions of source and target tokens variant/simplified Fellegi and Sunter seems to be a nice extension, since it is pretty obvious that disagreement on frequent terms is less important. two level distance function could be tempting to attempt followed by the paradigm of stacked / two stage learning. * On a side note: KMP (Knuth Morris Pratt) is worth mentioning for its lazy memoization approach. [[http://scitation.aip.org/getabs/servlet/GetabsServlet?prog=normal&id=SMJCAT000006000002000323000001&idtype=cvips&gifs=yes| Knuth et al.'77]] blocking a.k.a branch and bound ! this method reminds me of the suffix tree algorithm .

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