Paper: Subword Variation in Text Message Classification

ACL ID N10-1075
Title Subword Variation in Text Message Classification
Venue Human Language Technologies
Session Main Conference
Year 2010
Authors

For millions of people in less resourced re- gions of the world, text messages (SMS) pro- vide the only regular contact with their doc- tor. Classifying messages by medical labels supports rapid responses to emergencies, the early identification of epidemics and everyday administration, but challenges include text- brevity, rich morphology, phonological vari- ation, and limited training data. We present a novel system that addresses these, working with a clinic in rural Malawi and texts in the Chichewa language. We show that model- ing morphological and phonological variation leads to a substantial average gain of F=0.206 and an error reduction of up to 63.8% for spe- cific labels, relative to a baseline system opti- mized over word-sequences. By comparison, there is no significant gain wh...