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 ) introduced an unsupervised noisy channel model that considered several word formation processes in a generative model.
An Unsupervised Model for Text Message Normalization
Proceedings of the Workshop on Computational Approaches to Linguistic Creativity
Computer Science