“President Vows to Cut Taxes Hair”:Dataset and Analysis of Creative Text Editing for Humorous Headlines
Table 5: Classification accuracy for various funniness-sorted dataset proportions and classifiers/feature sets. MaxUF is the highest score for the not-funny class, MinF is the lowest score for the funny class, and we also provide Krippendorff’s α for judge agreement.
['[ITALIC] X', 'MaxUF', 'MinF', '[ITALIC] α', 'Rf-Glv', 'Rf-ngram', 'Lstm-Glv']
LSTM performs better than random forest with either n-gram (Rf-ngram) or GloVe features (Rf-Glv), achieving our best accuracy of 68.54% using X = 10. We suspect that the reason for the LSTM’s superior performance is that it learns predictive interactions between the semantics of the headline’s words (via the GloVe embeddings)