User Guide

198 SYSTRAN Desktop 7 User Guide
Translation Memories
Translation Memories are available for use only in SYSTRAN
Business Translator and SYSTRAN Premium Translator.
Translation Memories (TMs) are databases of paired sentences that have been pre-
translated. During the translation process, TM entries are matched with sentences in the
source text. These entries can be formatted (for example, italic or bold) through the SDM
Formatting toolbar.
Normalization Dictionaries
Normalization Dictionaries are available for use only in SYSTRAN Premium
Translator.
There are two types of Normalization Dictionaries (NDs): source normalization and target
normalization.
Source normalization is applied to a source file before translation.
It can be used, for instance, to:
• Standardize terminology in the source text: for example, you can define that “colour”
should be normalized to “color” as well as its inflected forms (such as “colours” to
“colors”).
• Expand abbreviations. In email language or chats, for example, “4u” can be normalized to
“for you” before translation, so that it is correctly processed by the translation engine.
Target normalization adapts translation output to user needs for terminology consistency. It
also provides a way to replace sequences generated by the software with user-defined
sequences.
Since normalization dictionaries are applied both before and after the translation process, it is
possible to make use of the coding category “sequence” without breaking the sentence
analysis.
Lookup Operators
Lookup Operators are available for use only in SYSTRAN Premium Translator.
You can simplify your User Dictionary by reducing the number of entries through the use of
Lookup operators. Each operator represents a certain pattern or range of characters that can
be found in an expression or in a User Dictionary. For example, the Number operator
represents any number.
There are three types of predefined Lookup operators:
URL operator
Match a URL (file path or Web URL)
Find operator
Use the Find operator to help reduce the size of a dictionary by factorizing