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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.
Comparison of text files, binary files, and directories; Three- and two-way diff and merge; Table comparison; File list comparison; Difference highlighting to the level of lines, words or characters. Syntax highlighting; Fuzzy line matching; Ability to recognize moved text blocks; Manual synchronization; Shell integration in 32-bit and 64-bit ...
The basic linguistic assumption of proximity searching is that the proximity of the words in a document implies a relationship between the words. Given that authors of documents try to formulate sentences which contain a single idea, or cluster of related ideas within neighboring sentences or organized into paragraphs, there is an inherent, relatively high, probability within the document ...
Based on text analyses, semantic relatedness between units of language (e.g., words, sentences) can also be estimated using statistical means such as a vector space model to correlate words and textual contexts from a suitable text corpus. The evaluation of the proposed semantic similarity / relatedness measures are evaluated through two main ways.