Editing Dictionary-based machine translation (section)

== Translingual information retrieval ==
"Translingual information retrieval (TLIR) consists of
providing a query in one language and searching document collections in one or
more different languages". Most methods of TLIR can be quantified into two
categories, namely statistical-IR approaches and query translation. Machine
translation based TLIR works in one of two ways. Either the query is translated
in the target language, or the original query is used to search while the
collection of possible results is translated in the query language and used for
cross-reference. Both methods have pros and cons, namely:<ref name=":6">{{Cite journal|title = Translingual information retrieval: learning from bilingual corpora|date = August 1998|publisher = Language Technologies Institute, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA|author1=Yiming Yang |author2=Jaime G. Carbonell |author3=Ralf D. Brown |author4=Robert E. Frederking |doi=10.1016/S0004-3702(98)00063-0 |volume=103 |issue = 1–2|journal=Artificial Intelligence |pages=323–345|doi-access=free }}</ref>
* Translation Accuracy – the correctness of any machine translation, is dependent on the size of the translated text, thus short texts or words may suffer from a bigger degree of semantic errors, as well as lexical ambiguities, whereas a larger text may provide context, which helps at disambiguation.
* Retrieval Accuracy – based on the same logic invoked at the previous point, it is preferably to have whole documents translated, rather than queries, because large texts are likely to suffer from less loss of meaning in translation then short queries.
* Practicality – unlike the previous points, translating short queries is the best way to go. This is because it is easy to translate short texts, whilst translating whole libraries is highly resource intensive, plus the volume of such a translating task implies the indexing of the new translated documents

All this points prove the fact that Dictionary-Based machine translation is the most efficient and reliable form of translation when working with TLIR. This is because the process "looks up each query term in a general-purpose bilingual dictionary, and uses all its possible translations."<ref name=":6" />