Humor in Word Embeddings:Cockamamie Gobbledegook for NincompoopsWARNING: This paper contains words that people rated humorous including many that are offensive in nature.
Table 1: Correlation coefficients compared across embeddings using least-squares regression to predict EH humor ratings. Correlations with 10-fold cross-validation and confidence intervals also reported.
['Embedding name', 'Corr.', 'Corr. (using CV)']
Hence, we begin by fitting a simple least-squares linear regression model, predicting the EH mean humor rating for each word in the dataset from its 300-dimensional embedding. The resulting “humor vector” is of course not humorous as a collection of 300 numbers, but its inner-product with other word vectors is found to correlate significantly with humor. The first column uses all EH words for fitting the linear model and for evaluation but is prone to overfitting since the models have 300 dimensions. For the second column, 10-fold cross validation is used to predict the labels (so each fold is predicted separately as hold-out) and then the correlation between the predictions and EH ratings is computed. Finally, the process is repeated 100 times to compute mean correlations and 95% confidence intervals.