An effective semi-supervised extractor will have good performance over a range of extraction tasks and corpora . However , many of the learning procedures just cited have been tested on only one or two extraction tasks , so their generality is uncertain . To remedy this , we have tested learners based on both assumptions , targeting both a MUC ( Message Understanding Conference ) scenario and several ACE ( Automatic Content Extraction ) event types . We identify shortcomings of the prior bootstrapping methods , propose a more effective and stable ranking method , and consider the effect of different corpora and evaluation metrics .