(translation memory segment)Īs fuzzy matches with a low percentage are usually not particularly useful for a translator, CAT and TEnT tools typically allow you to set a fuzzy match threshold. Note: some tools may only look at the preceding segment when determining a context match. the preceding and following segments are also exact matches). (translation memory segment)Ī context match (aka in-context match, 101% match) is when your segment is an exact match to a segment in your translation memory, including its context (i.e. What exactly does the percentage mean - well, that is the million dollar question that I'll explore in more detail below.Īn exact match (aka 100% match) is when your source segment matches word for word a segment in your translation memory. By leveraging fuzzy matching in this example, a translator would only have to translate one word instead of the whole sentence.Ī fuzzy match can be any match between 1% and 99%. (translation memory segment)Īs you can see, except for the name highlighted in orange, the sentences are the same. Take for example the following sentences: If your tool only showed you exact matches (aka 100% matches) you would be missing out. With your favorite CAT or TEnT tool you can utilize fuzzy matches to get more leverage from your translation memory. Although I can't give you the answer to how other tools calculate their fuzzy matches, hopefully I can shed some light on the different possible ways a fuzzy match could be calculated. Today's post will look at some of the factors that might come into play when calculating a fuzzy match. For good reason, as CAT tool vendors seem to be overly protective of their calculation. Most translators have probably heard of fuzzy matches (a similar, but not identical, match to the source segment found in a translator's translation memory ) however, few understand exactly how a fuzzy match is calculated.
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