Framework for Improving English to Hindi Rule-Based Translation System
Keywords:
Natural Language Processing (NLP), Machine Translation (MT), Rule Based Machine Translation (RBMT), Morphological Analyzer (MA), Lexical Resource (LR)Abstract
Presently the information captured through Morpheme, Lexeme or Word-based Morphological Analysis, for word or words in phrases is not enough for Natural Language Processing (NLP) systems as words have different meanings as individuals or in groups. Since some phrases are well structured, sentence level morphological analyzer provides effective knowledge base for NLP. This paper presents work of phrase level and word level morph analyzers for English-Hindi language pair(s) of (most vibrant) tourism domain. The approach proposed is of identifying unique sentence structures capable of representing complete targeted corpus. First the available corpus is used to analyze sentence structures with the help of available and developed IT tools to provide the necessary information such as occurrence of the “group of words”, classify these group of words into various grammatical categories, study their behavior in rule-based machine translation system, find out the divergence between human and machine interpretation and find suitable rules to reduce the divergence. This captured intelligence can be useful as knowledge base for NLP systems.
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