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The closeness of a match is measured in terms of the number of primitive operations necessary to convert the string into an exact match. This number is called the edit distance between the string and the pattern. The usual primitive operations are: [1] insertion: cot → coat; deletion: coat → cot; substitution: coat → cost
When an exact match cannot be found in the TM database for the text being translated, there is an option to search for a match that is less than exact; the translator sets the threshold of the fuzzy match to a percentage value less than 100%, and the database will then return any matches in its memory corresponding to that percentage.
Likewise, many early operating systems do not support multiple encoding formats and thus will end up displaying mojibake if made to display non-standard text—early versions of Microsoft Windows and Palm OS for example, are localized on a per-country basis and will only support encoding standards relevant to the country the localized version ...
Spoilers ahead! We've warned you. We mean it. Read no further until you really want some clues or you've completely given up and want the answers ASAP. Get ready for all of today's NYT ...
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Not just for the obvious ramifications of this season. But because losing brought up the old questions with their quarterback they've been trying to outrun. The Colts were practically playing for ...
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The higher the Jaro–Winkler distance for two strings is, the less similar the strings are. The score is normalized such that 0 means an exact match and 1 means there is no similarity. The original paper actually defined the metric in terms of similarity, so the distance is defined as the inversion of that value (distance = 1 − similarity).