  Query term matching with document term matching is the basic function of any
best effort Information Retrieval models like Vector Space Model. In our
problem of SMS based Information Systems we expect common people to participate
in information search. Our system allows mobile users to formulate their
queries in their own words, own transliteration style and spelling formation.
To achieve this flexibility we have resolved the term level ambiguity due to
inherent transcription noise in user query terms. We have developed a rule
based approach to select most relevantly close standard term for each noisy
term in the user query. We have used four different versions of the rule based
algorithm with variation in the rule set. We have formulated this rule set
including the basic Levenshtein minimum edit distance algorithm for term
matching. This paper presents the experiments and corresponding results of
Marathi and Hindi language literature information system. We have experimented
on Marathi and Hindi literature which include songs, gazals, powadas, bharud
and other types in a standard transliteration form like ITRANS.
