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Methods of implementing MT

Machine translation can use a method based on linguistic rule, which means that words will be translated in a linguistic way — the most suitable (orally speaking) words of the target language will replace the ones in the source language.

There are many ways and methods that can be used for language translation.

1. Dictionary-based machine translation

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.

2. Statistical machine translation

Statistical machine translation tries to generate translations using statistical methods based on bilingual text corpora, such as the canadian Hansard corpus, the English-French record of the Canadian parliament. Where such corpora are available, impressive results can be achieved translating texts of a similar kind, but such corpora are still very rare.

3. Example-based machine translation

Example-based machine translation (EBMT) approach is often characterised by its use of a bilingual corpus as its main knowledge base, at run-time. It is essentially a translation by analogy and can be viewed as an implementation of case-based reasoning approach of machine learning.

4. Interlingual machine translation

Interlingual machine translation is one instance of rule-based machine translation approaches. According to this approach, the source language, ie. the text to be translated is transformed into an interlingual, i.e. source/target language independent representation. The target language is then generated out of the interlingua.

कपिराइट dobhase@ku.edu.np इन्फरमेशन एन्ड लेङग्वेज प्रोशेसिंग रिसर्च ल्याब (एन. एल. पि.) काठमाण्डु विश्वविध्यालय काभ्रे, धुलिखेल