Abstract:
The Short Messaging Service (SMS) is built on the ability of mobile telephones tosend and receive text messages. SMS based applications are increasing dramatically day byday in the telecommunications industry. The most common use of SMS is for notifying mobile phone users that they have new voice or fax mail messages waiting. Whenever anew message is dispatched into the mailbox, an alert by SMS informs the user of this fact.The Short Message Service can also be used to deliver a wide range of information tomobile phone users from share prices, match scores, weather, flight information, news headlines, lottery results, jokes. In general, user interaction based SMS services requestsome predefined keywords from the users and respond to them after processing theirmessages.However, most users think that they are communicating not with a machine but withhumans, so they compose misspelled and/or machine specific messages containing more than just the needed keywords. As a result, they receive error messages from the server andgenerally do not continue to use the software after trying two or three times by makingsame mistakes. In this thesis, I introduce a new Short Message Service (SMS) parsing model usingStatistical NLP Techniques, whose aim is to solve the existing SMS user subscriptionproblem of a real software company. To do this, the N-Gram statistical approach will beused.
