Processing Named Entities in Text

Understanding human communication is a key foundation on which the understanding of human dynamics is based. Detection and classification of names in text and resolving mentions of those names to real-world entities are language-understanding tasks that might reasonably be automated. The need for these techniques arises in numerous settings such as news monitoring, law enforcement, and national security. In this article we give an overview of research in the area, describe automated techniques used for identifying and relating names in text, and discuss community evaluations that have given a significant boost to research efforts worldwide. We also highlight APL's contributions to research into some of these problems, giving particular emphasis to a recent evaluation of algorithms to match entities in text against a large database.