Applying CLP to machine translation: a Greek case study

Переведенное название: Применяя CLP в машинном переводе: на примере греческого

Результат исследований: Научные публикации в периодических изданияхстатья


In this research we concentrate on how CSP techniques can contribute to computational linguistics. For this purpose, we tested CP techniques on a linguistic problem of polysemy in machine translation. Ambiguity arises almost every time computers try to deal with human language. In information retrieval, the computer often provides information about alternative meanings of the search terms, meanings that we have no interest in. In machine translation, different meanings of an English word may be expressed by very different words in the target language, so it is important to determine which meaning of the English word the author had in mind and this is where a computer fails. Over and over, attempts to use computers to process human language have been frustrated
by the computer’s limited ability to deal with polysemy (Miller 2001). A toy system has been designed for translating between English and Greek. It is based on 6 sentences that, using the same lexicon, can be expanded to slightly more phrases. The translation is based on the results of two parsers — the English and the Greek one. Parsing uses a set of onstraints (both semantic and grammatical). The main objective of this research is to evaluate Backtracking as one of CSP algorithms for machine translation. We used PROLOG to write an application because it is the only programming language that supports backtracking and where the choice made by the program can be traced back at every stage.

Переведенное названиеПрименяя CLP в машинном переводе: на примере греческого
Язык оригиналаанглийский
Страницы (с-по)69-79
ЖурналJournal of applied linguistics and lexicography
Номер выпуска1
СостояниеОпубликовано - 2019

Предметные области Scopus

  • Гуманитарные науки и искусство (все)

Ключевые слова

  • Applied linguistics
  • machine translation
  • Greek

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