Identifying a wide range of events in free text is an important, open challenge for NLP. Previous approaches to relation extraction have focused on a limited number of relations, typically the static relations found in Wikipedia InfoBoxes, but these methods don't scale to the number and variety of events in Web text and news streams. We are developing an approach that automatically detects event relations from news streams and learns extractors for those events. Our NewsSpike system leverages properties of news text to learn high-precision event extractors.