Are Word Embedding-based Features Useful for Sarcasm Detection?
Table 1: Similarity scores for all pairs of content words in ‘A woman needs a man like a fish needs bicycle’
['[EMPTY]', '[BOLD] man', '[BOLD] woman', '[BOLD] fish', '[BOLD] needs', '[BOLD] bicycle']
The similarity score is the cosine similarity between vectors of two words. To illustrate our features, we use our example ‘A woman needs a man like a fish needs a bicycle’. Using these similarity scores, we compute two sets of features: Unweighted similarity features (S): We first compute similarity scores for all pairs of words (except stop words). We then return four feature values per sentence. Maximum score of most similar word pair Minimum score of most similar word pair Maximum score of most dissimilar word pair Minimum score of most dissimilar word pair For example, in case of the first feature, we consider the most similar word to every word in the sentence, and the corresponding similarity scores. Thus, the first feature in case of our example would have the value 0.766 derived from the man-woman pair and the second feature would take the value 0.078 due to the needs-man pair. The other features are computed in a similar manner. Distance-weighted similarity features (WS): Like in the previous case, we first compute similarity scores for all pairs of words (excluding stop-words). For all similarity scores, we divide them by square of distance between the two words. Thus, the similarity between terms that are close in the sentence is weighted higher than terms which are distant from one another. Thus, for all possible word pairs, we compute four features: Maximum distance-weighted score of most similar word pair Minimum distance-weighted score of most similar word pair Maximum distance-weighted score of most dissimilar word pair Minimum distance-weighted score of most dissimilar word pair These are computed similar to unweighted similarity features.