Context based word prediction for texting language

  • Authors:

  • Sachin Agarwal;Shilpa Arora

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • Large Scale Semantic Access to Content (Text, Image, Video, and Sound)
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.