To solve this classification problem, we adopt a simple supervised learning strategy based on state-of-the-art characteristics, plus features from a time-augmented version of WordNet.
each pair of entities to be classified as BE-FORE, AFTER, INCLUDES or NA-RELATION is encoded with the following features: -String features: the tokens and lemmas of each entity pair; -Grammatical features: the part-of-speech tags TWnP TWnH MC2 Sentence classification (p,r,f1) (69.7,66.1,66.7) (68.2,70.5,69.3) (69.8,67.6,68.6) (73.3,70.1 71.4) Tweet classification (p,r,f1) (51.4,47.1,49.1) (50.4,52.8,51.5) (51.8,48.2,49.8) (52.8,50.6,51.6) Table 7: Results for temporal sentence and tweet classification performed on 10-fold cross validation with SVM with Weka default parameters.