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SYSTRAN, founded by Dr. Peter Toma in 1968, [1] is one of the oldest machine translation companies. SYSTRAN has done extensive work for the United States Department of Defense and the European Commission.
Rule-based machine translation (RBMT; "Classical Approach" of MT) is machine translation systems based on linguistic information about source and target languages basically retrieved from (unilingual, bilingual or multilingual) dictionaries and grammars covering the main semantic, morphological, and syntactic regularities of each language respectively.
This method of Dictionary-Based Machine translation explores a different paradigm from systems such as LMT. An example-based machine translation system is supplied with only a "sentence-aligned bilingual corpus". [3] Using this data the translating program generates a "word-for-word bilingual dictionary" [3] which is used for further translation.
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Deutsches Rechtswörterbuch (Dictionary of Historical German Legal Terms) Lists of dictionaries cover general and specialized dictionaries, collections of words in one or more specific languages, and collections of terms in specialist fields. They are organized by language, specialty and other properties.
The actual design of the project was unusual as MT projects go. Older systems, such as SYSTRAN, were heavily dictionary-based, with minor support for rearranging word order. More recent systems have often worked on a probabilistic approach, based on parallel corpora.
Dictionaries and encyclopedias can be installed on Lingoes in the form of new add-ons to extend its functionality. Add-ons for Wikipedia , Baidu Baike , Longman Dictionary of Contemporary English , Merriam-Webster's Collegiate Dictionary , WordNet , MacMillan English Dictionary , Collins English Dictionary and other cross-English dictionaries ...
It recommends that tools be developed to aid translators — automatic dictionaries, for example — and that some research in computational linguistics should continue to be supported. [6] [7] The report causes a significant decline in government funding for machine translation in the US, and to a lesser extent in the UK and Russia. 1968