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* Machine translator based CLIR techniques
CLIR systems have improved so much that the most accurate multi-lingual and cross-lingual [[adhoc information retrieval]] systems today are nearly as effective as monolingual systems.<ref>Oard, Douglas.
Mostly, the various mechanisms of [[Linguistic typology|variation in human language]] pose coverage challenges for information retrieval systems: texts in a collection may treat a topic of interest but use terms or expressions which do not match the expression of information need given by the user. This can be true even in a mono-lingual case, but this is especially true in cross-lingual information retrieval, where users may know the target language only to some extent. The benefits of CLIR technology for users with poor to moderate competence in the target language has been found to be greater than for those who are fluent.<ref>{{cite journal|last1=Airio|first1=Eija|title=Who benefits from CLIR in web retrieval?|journal=Journal of Documentation|date=2008|volume=64|issue=5|pages=760–778|doi=10.1108/00220410810899754|url=http://tampub.uta.fi/handle/10024/65923}}</ref> Specific technologies in place for CLIR services include [[Morphology (linguistics)|morphological analysis]] to handle [[inflection]], decompounding or compound splitting to handle [[Compound (linguistics)|compound terms]], and translations mechanisms to translate a query from one language to another.
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