A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously
Table 4: Bootstrapping Using VerbOcean Patterns
['[EMPTY]', '0 (Seeds)', '1', '2', '3', '4', 'Total']
Therefore, we replace our two straightforward temporal relation patterns, EV_A after (before) EV_B, with the ten patterns proposed by VerbOcean and use these patterns to acquire seed regular event pairs. However, with exactly the same settings and frequency threshold we used in seed identification, we can only identify seven seed regular event pairs using the same complete Gigaword corpus. In order to obtain more seed event pairs, we lowered the frequency threshold of seeing an event pair in patterns from ten to three. Then with the initial 112 seed regular event pairs, around 500 new event pairs were later learned using exactly the same bootstrapping learning settings we have used. In total, only 657 event pairs were learned by using VerbOcean patterns. Note that the Gigaword corpus we used is much smaller in volume than the Web. Therefore, we hypothesize that VerbOcean patterns are too specific to be productive in identifying regular event pairs from a limited text corpus.