A Normalized Levenshtein Distance Metric

By Yujian Li, Liu Bo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007

917 citations recorded by the source corpus.

How researchers see this paper

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Discrepancy Analysis of State Sequences (2011, methodology): “We use data and the research question from McVicar and Anyadike-Danes’ (2002) study of the school-to-work transition in Northern Ireland.”
Dissecting Ponzi schemes on Ethereum: identification, analysis, and impact (2017, methodology): “We use the normalized Levenshtein distance [19] (NLD) as a measure of similarity between two bytecode ﬁles 12 .”
A novel approach for the structural comparison of origin-destination matrices: Levenshtein distance (2020, neutral): “This measure always lies between 0 and 1 (Yujian and Bo, 2007 Heeringa (2004) developed a pseudo code for computing GLD and NLD for comparing two strings.”
What matters in differences between life trajectories: a comparative review of sequence dissimilarity measures (2016, background): “The OM distance between the sequences is a metric if Γ defines a metric between the admissible states (Yujian and Bo, 2007).”
Retrieval And Structuring Augmented Generation with Large Language Models (2025, unknown): “Editdistance-basedRetrievalmatches queries and documents by comparing edit distances between words and phrases like Levenshtein Distance [69, 77, 282].”