Causal Explanation Analysis on Social Media
Table 4: Feature ablation test of Linear SVM for causality prediction
['[BOLD] Model', 'F1']
POS tags caused the largest drop in F1. We suspect POS tags played a unique role because discourse connectives can have various surface forms (e.g., because, cuz, bcuz, etc) but still the same POS tag ‘P’. Also POS tags can capture the occurrences of modal verbs, a feature previously found to be very useful for detecting similar discourse relations Pitler et al. N-gram features caused 0.022 F1 drop while sentiment tags did not affect the model when removed. Unlike the previous work where First-Last, First3 and Word pairs tended to gain a large F1 increase for multiclass discourse relation prediction, in our case, they did not affect the prediction performance compared to other feature types such as POS tags or N-grams.