Phrase, sentence, paragraph, or sentence-like piece of text that represents a cognitive unit and is used when searching for a match in a translation memory (TM) database.
Discrete segments of text show up repeatedly across various pieces of text. Matching source segments in the TM for which previously approved translations exist increases efficiency in the translation process by providing the relevant translation to the translator.
A conventional set of symbols that visually represent sounds and ideas.
Scripts, or writing systems, are the framework for conveying meaning graphically, and all written words use some form of script. These systems are classified into three types: logographic, syllabic, and segmental[Daniels 1996][Nakanishi 1989]. Encoding standards such as ISO/IEC 10646, and Unicode, have been developed as a way of representing most, (but certainly not all), scripts in common use today[Unicode]. Of the 7,000+ languages spoken today, roughly a third are considered endangered. Many of these either don’t have scripts or are not supported.
The process whereby a human translator edits machine-translated output to achieve an acceptable level of quality in the final product.
Machine translation (MT) is frequently used in the translation industry because of its gains in speed and scale. Yet, there is often a loss in quality. Human post-editing improves MT output, bringing the quality up to acceptable levels.
Technical activities required to support the process of translation, including converting files to a translatable format, re-converting them to their original formats after translation, and ensuring technical validity of translated files.
Localization engineering is a key component of the localization process and often included as a cost item in estimates, but it is frequently misunderstood or misinterpreted.
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The instance when a source segment that is submitted to translation is partially recognized as being similar to a source segment that has already been translated and exists in the translation memory (TM).
Fuzzy match is important for translation costs and efficiency. As a TM builds up a library of content, it will recognize more fuzzy matches, leading to reduced costs and increased efficiency.