Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection
TABLE I: Results of our approach for dataset of tweets by Riloff et al. (2013), compared with best reported values (Joshi et al. (2015) and Riloff et al. (2013)) on the same dataset
['Riloff et al. (2013)', '[EMPTY]', '[BOLD] P 62', '[BOLD] R 44', '[BOLD] F 51']
When word2vec similarity is used for the all-words approach, an F-score of 54.48% is obtained. The best F-score of 80.24% is obtained when WordNet is used as the similarity measure in our Incongruous words-only approach. We observe that the Incongruous words-only approach performs significantly better than the all-words approach. In case of word2vec similarity, the F-score increases by around 18%, and by around 9% in case of WordNet similarity. Also, the optimal threshold values are lower for the all-words approach as compared to the Incongruous words-only approach.