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Even the best information retrieval systems miss a large amount of relevant material. This work describes an approach that adds some artificial intelligence to the task of improving the results. We combine shallow parsing and information extraction techniques with conventional information retrieval to build a richer database of events to support the retrieval effort. In our system each event has three main types of identifying factors: keywords that indicate that an interesting event has occurred, dates that specify the time of the event and proper nouns that specify the people, organizations, locations, products, etc., that are involved in the event. In This work we describe a system that uses these keys to find events, classify them, and save them in a database along with the identifier of the document that mentions the event. The retrieval process uses this information to provide the user with menus to form queries about the events; it then executes those queries, and finds the related documents.