However, if we have, for example textA = "stackoverflow is cool"
Nice! the letter 'e' that is common to red and blue causes the diff_main() to see this area of the text as four edits, but the cleanupSemantic() fixes as just two edits, nicely singling out the different sems 'blue' and 'red'. rather than actual NLP processing based on lexicons and other semantic-level devices.įor example, the textA and textB values used above produce the following "before-and-after-diff_cleanupSemantic" values for the diffs array The results produced however are far from perfect, as this processing is just simple heuristics based on the length of differences and surface patterns etc.
# and if you want the results as some ready to display HMTL snippetĪ word on " semantic" processing by diff-match-patchīeware that such processing is useful to present the differences to a human viewer because it tends to produce a shorter list of differences by avoiding non-relevant resynchronization of the texts (when for example two distinct words happen to have common letters in their mid). # diff_cleanupSemantic() is used to make the diffs array more "human" readable # All 'diff' jobs start with invoking diff_main() # and complexity, you may want to extend (or here suppress) theĭmp.Diff_Timeout = 0 # or some other value, default is 1.0 seconds # Depending on the kind of text you work with, in term of overall length In the case of a simple "semantic" comparison this is what you need import diff_match_patchĭmp = diff_match_patch.diff_match_patch() Therefore one can typically use sample snippets in languages other than one's target language to figure out which particular API calls are needed for various diff/match/patch tasks. Google's diff-match-patch API is the same for all languages that it is implemented in (Java, JavaScript, Dart, C++, C#, Objective C, Lua and Python 2.x or python 3.x).
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