  Normalization of SMS text, commonly known as texting language, is being
pursued for more than a decade. A probabilistic approach based on the Trie data
structure was proposed in literature which was found to be better performing
than HMM based approaches proposed earlier in predicting the correct
alternative for an out-of-lexicon word. However, success of the Trie based
approach depends largely on how correctly the underlying probabilities of word
occurrences are estimated. In this work we propose a structural modification to
the existing Trie-based model along with a novel training algorithm and
probability generation scheme. We prove two theorems on statistical properties
of the proposed Trie and use them to claim that is an unbiased and consistent
estimator of the occurrence probabilities of the words. We further fuse our
model into the paradigm of noisy channel based error correction and provide a
heuristic to go beyond a Damerau Levenshtein distance of one. We also run
simulations to support our claims and show superiority of the proposed scheme
over previous works.
