Probabilistic language model for template messaging based on Bi-gram
Rina Damdoo, Urmila N. Shrawankar · IEEE-International Conference On Advances In Engineering, Science And Management · 2012
This paper reports the benefits of Probabilistic language modeling in template messaging domain. Through a Statistical Machine Translation (SMT) sentences written with short forms, misspelled words and chatting slang can be corrected. Given a source-language (e.g., Short message) sentence, the problem of machine translation is to automatically produce a target-language (e.g., Long form English) translation, to be used by the young generation for messaging. The main goal behind this project is to analyze the improvement in efficiency as the size of bilingual corpus increases. Machine learning and translation systems, dictionary and textbook preparations, patent and reference searches, and various information retrieval systems are the main applications of the project.