An improved Bayesian TRIE based model for SMS text normalization
This work addresses the problem of improving SMS text normalization for natural language processing applications, but it appears incremental as it builds upon an existing Trie-based approach.
The authors tackled SMS text normalization by proposing a structural modification to an existing Trie-based model with a novel training algorithm and probability generation scheme, proving statistical properties and showing superiority over previous works through simulations.
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.