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Document comparison, also known as redlining or blacklining, is a computer process by which changes are identified between two versions of the same document for the purposes of document editing and review. Document comparison is a common task in the legal and financial industries.
The rsync protocol uses a rolling hash function to compare two files on two distant computers with low communication overhead. File comparison in word processors is typically at the word level, while comparison in most programming tools is at the line level. Byte or character-level comparison is useful in some specialized applications.
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 other text comparison algorithms. Diff-Text will always match up significant similarities even if contained within non-identical or moved lines.
A critical consideration is how the two files being compared must be substantially similar and thus not radically different. Even different revisions of the same document — if there are many changes due to additions, removals, or moving of content — may make comparisons of file changes very difficult to interpret.
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.
Cosine similarity can be seen as a method of normalizing document length during comparison. In the case of information retrieval, the cosine similarity of two documents will range from , since the term frequencies cannot be negative. This remains true when using TF-IDF weights. The angle between two term frequency vectors cannot be greater than ...
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The information content of a concept (term or word) is the logarithm of the probability of finding the concept in a given corpus. only considers the information content of lowest common subsumer (lcs). A lowest common subsumer is a concept in a lexical taxonomy ( e.g. WordNet), which has the shortest distance from the two concepts compared.