A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously
Table 2: Accuracy of 100 Randomly Selected Event Pairs
['Systems', 'Seed Pairs', 'New Pairs']
We determine that an event pair is correctly predicted by a system if the system predicted temporal relation is the same as the label that has been assigned by both of the two annotators. The overall kappa inter-agreement between the two annotators is 72%. We can see that with event argument generalization, the quality of acquired seed regular event pairs is roughly equal to that using specific name arguments. Furthermore, because we obtained two times of seed event pairs after using event argument generalization, the second and third bootstrapping systems received more guidance and continued to learn regular event pairs with a high quality. In addition, using dependency path based sentential contexts enables the classifier to further improve the accuracy of bootstrapped regular event pairs.