Probabilistic N-gram language model for SMS Lingo

This paper presents a pioneering step in designing Bi-Gram based decoder for SMS Lingo. In the last few years, a significant increment in both the computational power and storage capacity of computers, and the availability of large volumes of bilingual data, have made possible for Statistical Machine Translation (SMT) to become an actual and practical technology. This paper employs Bi-Gram Language Model (LM) with a SMT decoder through which a sentence written with short forms in an SMS is translated into long form sentence. Here the results over a development and test set are analyzed and commented. The main objective behind this project is to analyze the improvement in efficiency as the size of bilingual corpus increases.