Cell phone text messaging users express themselves briefly and colloquially using a variety of creative forms. We analyze a sample of creative, non-standard text message word forms to determine frequent word formation processes in texting language. Drawing on these observations, we construct an unsupervised noisy-channel model for text message normalization. On a test set of 303 text message forms that differ from their standard form, our model achieves 59% accuracy, which is on par with the best supervised results reported on this dataset.
REF extended this model by introducing an unsupervised noisy channel model.
An Unsupervised Model for Text Message Normalization
Proceedings of the Workshop on Computational Approaches to Linguistic Creativity
Computer Science