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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:Bbd]]. This paper presents experimental comparison of various string distance metrics. They are evaluated on matching and clustering tasks. Following methods were considered : * Edit-distance like functions - Levenstein and Monge-Elkan * Token based distance function - Jaccard similarity, Jenson-shannon, SFS distance * Hybrid distance functions - soft TFIDF During the experimental study they found that * TFDF performs best among several token-based metrics * Monge-elkan is best among string edit-distance metrices and combination of TFIDF and jaro-Winkler performs better than either of them.

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