A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously
Table 1: Number of New Regular Event Pairs Generated after Each Bootstrapping Iteration
['Systems', '0 (Seeds)', '1', '2', '3', '4', '5', 'Total']
First, we can see that event argument generalization is useful in obtaining roughly two times of seed regular event pairs. Second, event argument generalization is useful in recognizing additional regular event pairs in bootstrapping learning as well. Third, dependency path based sentential contexts are effective in capturing relevant sentential contexts for temporal relation classification, which enables the bootstrapping system to maintain a learning momentum and learn more regular event pairs.