Context Based Word Prediction for Texting Language

The use of digital mobile phones has led to a tremendous increase in communication using SMS. On a phone keypad, multiple words are mapped to same numeric code. We propose a Context Based Word Prediction system for SMS messaging in which context is used to predict the most appropriate word for a given code. We extend this system to allow informal words (short forms for proper English words). The mapping from informal word to its proper English words is done using Double Metaphone Encoding based on their phonetic similarity. The results show 31% improvement over the traditional frequency based word estimation. Introduction The growth of wireless technology has provided us with many new ways of communication such as SMS (Short Message Service). SMS messaging can also be used to interact with automated systems or participating in contests. With tremendous increase in Mobile Text Messaging, there is a need for an efficient text input system. With limited keys on the mobile phone, multiple letters are mapped to same number (8 keys, 2 to 9, for 26 alphabets). The many to one mapping of alphabets to numbers gives us same numeric code for multiple words. Predictive text systems in place use the frequency-based disambiguation method and predict the most commonly used word above other possible words. T-9 (Text on 9-keys), developed by Tegic Communications, is one such predictive text technology used by LG, Siemens, Nokia Sony Ericson and others in their phones. iTap is another similar system developed and used by Motorola in their phones. T-9 system predicts the correct word for a given numeric code based on frequency. This may not give us the correct result most of the time. For example, for code ‘63’, two possible words are ‘me’ and ‘of’. Based on a frequency list where ‘of’ is more likely than ‘me’, T-9 system will always predict ‘of’ for code ‘63’. So, for a sentence like ‘Give me a box of chocolate’, the prediction would be ‘Give of a box of chocolate’. The sentence itself indeed gives us information about what should be the correct word for a given code. Consider the above sentence with blanks, “Give _ a box _ chocolate”. According to the English grammar, it is more likely that ‘of’ comes after a noun ‘box’ than ‘me’ i.e. it is more likely to see the phrase “box of” than “box me”. The algorithm proposed is an online method that uses this knowledge to correctly predict the word for a given code considering its previous context.