TFW, DamnGina, Juvie, and Hotsie-Totsie: On the Linguistic and Social Aspects of Internet Slang
Table 2. Performance of different models on Gold Standard Test set. Note that using character n-grams demonstrates the best performance.
['[BOLD] Model', '[BOLD] Weighted F1']
Observe that both the morpheme and the character n-gram models substantially outperform the baseline random classifier. Furthermore the model using character-ngram features significantly outperforms the morpheme based model. This is primarily because the morpheme based feature representation is too sparse for robust estimation of the decision boundary given the small amount of annotated training data. We believe that the morpheme features will be most effective when the amount of training data is much larger, to reduce the feature sparsity of general morpheme features. It is evident that the classifier does well on Alphabetisms, Clippings and Reduplicatives but is relatively worse at identifying Blends. We hypothesize this is because Blends are much more linguistically complex than other classes like Alphabetisms where distinctive features are much more easier to detect.