The paper focuses on Example Based Machine Translation (EBMT) system that translates sentences from English to Hindi. It uses the parallel corpus for translating sentences. Development of a machine translation (MT) system typically demands a large volume of computational resources. Requirement of computational resources (for example, rules) is much less in respect of EBMT. This makes development of EBMT systems for English to Hindi translation feasible, where availability of large-scale computational resources is still scarce. Example based machine translation relies on the database for its translation. The frequency of word occurrence is important for translation in EBMT in the following research.
Machine translation is a sub-field of computational linguistics that investigates the use of software to translate text or speech from one natural language to another. On a basic level, MT performs simple substitution of words in one natural language for words in another, but that alone usually cannot produce a good translation of a text, because recognition of whole phrases and their closest counterparts in the target language is needed. The paper focuses on Example Based Machine Translation (EBMT) system that translates sentences from English to Hindi. Development of a machine translation (MT) system typically demands a large volume of computational resources. For example, rule based MT systems require extraction of syntactic and semantic knowledge in the form of rules, statistics-based MT systems require huge parallel corpus containing sentences in the source languages and their translations in target language. Requirement of such computational resources is much less in respect of EBMT. This makes development of EBMT systems for English to Hindi translation feasible, where availability of large-scale computational resources is still scarce. Example based machine translation relies on the database for its translation. The frequency of word occurrence is important for translation.
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