Machine translation can use a method based on dictionary entries, which means that the words will be translated as a dictionary does – word by word, usually without much correlation of meaning between them. Dictionary lookups may be done with or without morphological analysis or lemmatisation. While this approach to machine translation is probably the least sophisticated, dictionary-based machine translation is ideally suitable for the translation of long lists of phrases on the subsentential (i.e., not a full sentence) level, e.g. inventories or simple catalogs of products and services.
It can also be used to expedite manual translation, if the person carrying it out is fluent in both languages and therefore capable of correcting syntax and grammar.
LMT, introduced around 1990, is a Prolog-based machine-translation system that works
on specially made bilingual dictionaries, such as the Collins English-German
(CEG), which have been rewritten in an indexed form which is easily readable by
computers. This method uses a structured lexical data base (LDB) in order to
correctly identify word categories from the source language, thus constructing
a coherent sentence in the target language, based on rudimentary morphological
analysis. This system uses "frames" to identify the position a certain word
should have, from a syntactical point of view, in a sentence. This "frames" are
mapped via language conventions, such as UDICT in the case of English.
In its early (prototype) form LMT uses three lexicons,
accessed simultaneously: source, transfer and target, although it is possible
to encapsulate this whole information in a single lexicon. The program uses a
lexical configuration consisting of two main elements. The first element is a
hand-coded lexicon addendum which contains possible incorrect translations. The
second element consist of various bilingual and monolingual dictionaries
regarding the two languages which are the source and target languages.
This method of Dictionary-Based Machine translation explores
a different paradigm from systems such as LMT.
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The Human Language Technology (HLT) course introduces methods and applications for language processing and generation, using statistical learning and neural networks.
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