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Montemayor J.,Apls Milton senhower Research Center | Diehl C.P.,Lawrence Livermore National Laboratory
Johns Hopkins APL Technical Digest (Applied Physics Laboratory) | Year: 2011

Every moment, millions of people worldwide are communicating and sharing content online. On a variety of new digital social media, including discussion forums, blogs, e-mails, and status updates, we express ourselves to enrich existing relationships and to establish new relationships that would be difficult or impossible to develop offline. The staggering volume of these digital social artifacts presents new opportunities to extend and enhance our conventional notions of "search." In this article, we develop the concept of social query concept to explore the interactions of informational and social components of these new types of queries. Source

Bos N.D.,Apls Milton senhower Research Center | Greenberg A.M.,Apls Milton senhower Research Center | Kopecky J.J.,Apls Milton senhower Research Center
Johns Hopkins APL Technical Digest (Applied Physics Laboratory) | Year: 2011

M odels of human dynamics on a national scale are a current area of research interest. High-quality models could improve the nation's ability to manage social and political conflicts, win counterinsurgency struggles, conduct peacekeeping missions, and even prevent conflicts before they begin. Such models can potentially be used for research, policy, training, and prediction. However, validation is particularly difficult because of the complexity of the domain and nascent state of research. Types of validation include grounding, calibration, and verification. This article describes two current APL efforts: the Social Identity Look-Ahead Simulation (SILAS) and the Green Country Model (GCM). SILAS is a research tool focused on simulating social identity conflicts within a nation, with Nigeria used as a test case. GCM is a policy exercise (a "wargame") that simulates civilian (Green) effects in a Red-versus-Blue conflict. This article describes usage of these models and efforts at validation, including grounding, calibration, and verification, for each model. Source

McNamee P.,Apls Milton senhower Research Center | Mayfield J.C.,Apls Milton senhower Research Center | Piatko C.D.,Apls Milton senhower Research Center
Johns Hopkins APL Technical Digest (Applied Physics Laboratory) | Year: 2011

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. Source

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