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  • × author_ss:"Garg, N."
  1. Argamon, S.; Whitelaw, C.; Chase, P.; Hota, S.R.; Garg, N.; Levitan, S.: Stylistic text classification using functional lexical features (2007) 0.00
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    Abstract
    Most text analysis and retrieval work to date has focused on the topic of a text; that is, what it is about. However, a text also contains much useful information in its style, or how it is written. This includes information about its author, its purpose, feelings it is meant to evoke, and more. This article develops a new type of lexical feature for use in stylistic text classification, based on taxonomies of various semantic functions of certain choice words or phrases. We demonstrate the usefulness of such features for the stylistic text classification tasks of determining author identity and nationality, the gender of literary characters, a text's sentiment (positive/ negative evaluation), and the rhetorical character of scientific journal articles. We further show how the use of functional features aids in gaining insight about stylistic differences among different kinds of texts.
  2. Haynes, S.R.; Kannampallii, T.G.; Larson, L.L.; Garg, N.: Optimizing anti-terrorism resource allocation (2005) 0.00
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    Abstract
    Since spring of 2002 we have been working an a methodology, decision model, and cognitive support system to aid with effective allocation of anti-terrorism (AT) resources at Marine Corps installations. The work has so far been focused an the military domain, but the model and the software tools developed to implement it are generalizable to a range of commercial and publicsector settings including industrial parks, corporate campuses, and civic facilities. The approach suggests that anti-terrorism decision makers determine mitigation project allocations using measures of facility priority and mitigation project utility as inputs to the allocation algorithm. The three-part hybrid resource allocation model presented here uses multi-criteria decisionmaking techniques to assess facility (e.g., building, hangar) priorities, a utility function to calculate antiterrorism project mitigation values (e.g., protective glazing, wall coatings, and stand-off barriers) and optimization techniques to determine resource allocations across multiple, competing AT mitigation projects. The model has been realized in a cognitive support system developed as a set of loosely coupled Web services. The approach, model, and cognitive support system have been evaluated using the cognitive walkthrough method with prospective system users in the field. In this paper we describe the domain, the problem space, the decision model, the cognitive support system and summary results of early model and system evaluations.