document: Entity categorization over large document collections Extracting entities ( such as people , movies ) from documents and identifying the categories ( such as painter , writer ) they belong to enable structured querying and data analysis over unstructured document collections . In this paper , we focus on the problem of categorizing extracted entities . Most prior approaches developed for this task only analyzed the local document context within which entities occur . In this paper , we significantly improve the accuracy of entity categorization by ( i ) considering an entity 's context across multiple documents containing it , and ( ii ) exploiting existing large lists of related entities ( e.g. , lists of actors , directors , books ) . These approaches introduce computational challenges because ( a ) the context of entities has to be aggregated across several documents and ( b ) the lists of related entities may be very large . We develop techniques to address these challenges . We present a thorough experimental study on real data sets that demonstrates the increase in accuracy and the scalability of our approaches .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O