Fuzzy logic is part of our lives. Most phenomena and parameters take on specific values only after much simplification. We use fuzzy logic every day - oftentimes without knowing it - to identify similarities between things, clarify meanings that are not expressed in numbers, etc. Meanwhile, we assign phenomena to specific classes according to similarity.

Translation works the same way - when translating or reading a source text, we often realize that it resembles an already translated fragment or sentence. This was taken advantage of decades ago when the first CAT systems were developed. Their main task was to store all the translations made by a given person or company inside a database (translation memory). This eliminated the need to ever translate the same fragment of text twice. What’s more, continuously improving search algorithms allow our translators to search through a translation memory for similar fragments of text.

This leads to fuzzy search, which translators can use to get suggestions that only partially match the analyzed text, but are accurate enough for modifying them to be less time consuming than translating from scratch. Fuzzy search allows a translator to obtain multiple results containing a specific phrase or piece of text, which is priceless when it comes to finding the right translation context.