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2011 Aerospace Conference
Information Extraction using Natural Language Processing (NLP) produces entities along with some of the relationships that may exist among them. To be semantically useful, however, such discrete extractions must be put into context through some form of intelligent analysis. This paper 1,2 offers a two-part architecture that employs the statistical methods of traditional NLP to extract discrete information elements in a relatively domain-agnostic manner, which are then injected into andoi:10.1109/aero.2011.5747547 fatcat:bgmnkonxgnaj3awh4avm5md5du