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English-Hindi Cross Language Query Translation and Disambiguation Using Most Salient Seed Word

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1351)

Abstract

Natural languages suffer from two types of ambiguity namely Lexical ambiguity and Syntactic ambiguity. This paper deals only in lexical ambiguity, ambiguity that arises when a word has two or more possible meanings. English language is no excuse. To translate English to Hindi query in Cross Language Information Retrieval, these ambiguous words need to be disambiguated properly for relevant Hindi language documents to be retrieved. This paper aims to find the most salient context word in the English query and use it as a single disambiguation feature in contrast of using the entire query as a context for disambiguation. When the entire query is used as a context for disambiguation, all the terms are assumed as equally important. Ideally this is not true always. All the terms in the source query are not as predicative of the word being translated as others and thus treating all query terms as uniformly important may not always be a wise decision. This paper aims to investigate this claim by proposing two methods which use either statistical mean or contribution ratio to find the best context seed word to disambiguate user query terms. The proposed methods are compared to baseline method which uses entire query as disambiguation feature. The proposed methods achieve 85% precision as compared to baseline method, which is quiet good and thus these methods can be used with high confidence for query translation and disambiguation instead of using entire query context as done by most of the researchers.

Keywords

  • Cross Language Information Retrieval (CLIR)
  • Translation ambiguity
  • Translation disambiguation
  • Word co-occurrence feature
  • Salient seed word

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Correspondence to Pratibha Maurya .

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Maurya, P. (2021). English-Hindi Cross Language Query Translation and Disambiguation Using Most Salient Seed Word. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_5

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