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The algorithm only reports the longest in-order run of text between two documents. Text moved out of the longest run of similarities is missed. Heuristics are not used. Any similarity between the two documents above the specified minimum will be reported (if detecting moves is selected). This is the main difference between Diff-Text and most ...
In information theory, linguistics, and computer science, the Levenshtein distance is a string metric for measuring the difference between two sequences. The Levenshtein distance between two words is the minimum number of single-character edits (insertions, deletions or substitutions) required to change one word into the other.
It operates between two input strings, returning a number equivalent to the number of substitutions and deletions needed in order to transform one input string into another. Simplistic string metrics such as Levenshtein distance have expanded to include phonetic, token , grammatical and character-based methods of statistical comparisons.
In computing, the utility diff is a data comparison tool that computes and displays the differences between the contents of files. Unlike edit distance notions used for other purposes, diff is line-oriented rather than character-oriented, but it is like Levenshtein distance in that it tries to determine the smallest set of deletions and insertions to create one file from the other.
Systems for text similarity detection implement one of two generic detection approaches, one being external, the other being intrinsic. [5] External detection systems compare a suspicious document with a reference collection, which is a set of documents assumed to be genuine. [6]
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The most efficient method of finding differences depends on the source data, and the nature of the changes. One approach is to find the longest common subsequence between two files, then regard the non-common data as an insertion, or a deletion. In 1978, Paul Heckel published an algorithm that identifies most moved blocks of text. [2]
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