EventMiner: Mining Events from Annotated Documents

Abstract

Events are central in human history and thus also in Web queries, in particular if they relate to history or news. However, ambiguity issues arise as queries may refer to ambiguous events differing in time, geography, or participating entities. Thus, users would greatly benefit if search results were presented along different events. In this paper, we present EventMiner, an algorithm that mines events from top-k pseudo-relevant documents for a given query. It is a probabilistic framework that leverages semantic annotations in the form of temporal expressions, geographic locations, and named entities to analyze natural language text and determine important events. Using a large news corpus, we show that using semantic annotations, EventMiner detects important events and presents documents covering the identified events in the order of their importance.