Filtered Ranking for Bootstrapping in Event Extraction

Several researchers have proposed semi-supervised learning methods for adapting event extraction systems to new event types. This paper investigates two kinds of bootstrapping methods used for event extraction: the document-centric and similarity-centric approaches, and proposes a filtered ranking method that combines the advantages of the two. We use a range of extraction tasks to compare the generality of this method to previous work. We analyze the results using two evaluation metrics and observe the effect of different training corpora. Experiments show that our new ranking method not only achieves higher performance on different evaluation metrics, but also is more stable across different bootstrapping corpora.