<![CDATA[Notice of Retraction-----------------------------------------------------------------------After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IAES's Publication Principles.We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.The presenting author of this paper has the option to appeal this decision by contacting ijeei.iaes@gmail.com.-----------------------------------------------------------------------Bilingual dictionaries have always been an important source of query translation in Cross Language Information Retrieval. Besides other issues bilingual translation suffers from ambiguity problem. To resolve this issue, several recent works have recommended the use of term co occurrence statistics. Same concept with a major modification is the focus of our work described here. Our work is based on the fact that all terms do not have same discriminating power in a query. To overcome such problem, our algorithm provides more weight to discriminating terms in the query and treats co occurrences of useful terms as more valuable than those of frequent terms. The paper also takes into account the concept of local context in formulating formula for co-occurrences statistics. In the experiments, our method achieved 85% of monolingual translation in terms of the mean average precision (MAP). The results are quiet encouraging as compared to other methods used for cross language information retrieval for Indian languages. ]]>
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