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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.
MT may be based on a set of linguistic rules, or on large bodies (corpora) of already existing parallel texts. Rule-based methodologies may consist in a direct word-by-word translation, or operate via a more abstract representation of meaning: a representation either specific to the language pair, or a language-independent interlingua.
The rule-based machine translation approach was used mostly in the creation of dictionaries and grammar programs. Its biggest downfall was that everything had to be made explicit: orthographical variation and erroneous input must be made part of the source language analyser in order to cope with it, and lexical selection rules must be written ...
Grammatical abbreviations are generally written in full or small caps to visually distinguish them from the translations of lexical words. For instance, capital or small-cap PAST (frequently abbreviated to PST) glosses a grammatical past-tense morpheme, while lower-case 'past' would be a literal translation of a word with that meaning.
The theory first appeared in an article published by linguist Hans Josef Vermeer in the German Journal Lebende Sprachen, 1978. [2]As a realisation of James Holmes’ map of Translation Studies (1972), [3] [4] skopos theory is the core of the four approaches of German functionalist translation theory [5] that emerged around the late twentieth century.
Translation memory – database that stores so-called "segments", which can be sentences, paragraphs or sentence-like units (headings, titles or elements in a list) that have previously been translated, in order to aid human translators. Example-based machine translation – Rule-based machine translation –
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The translation is not necessarily in alignment with the morpheme segmented line (e.g., camel is the last word in the translation but the second word in the morpheme segmented line). Some words in the morpheme segmented line have multiple correspondences in the gloss (e.g., anu:be.NEG).
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