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The ability to quickly produce actionable intelligence from unanticipated, multiple, varied data sets require research advances in two key areas: (1) alignment of data models; and (2) advanced analytic algorithms. Making advances in these two research areas, and fully characterizing the performance of the research results, is the focus of this project, which is part of the IARPA Knowledge Discovery and Dissemination (KDD) Program.
IARPAs Foresight and Understanding from Scientific Exposition (FUSE) Program seeks to develop automated methods that aid in the systematic, continuous, and comprehensive assessment of technical emergence using publicly available information found in published scientific, technical and patent literature. SRIs team uses a theory-directed extension of best-of-breed language and network modeling techniques to produce the Copernicus system. SRIs CSTED leads the theory of emergence task.
The following are in reverse chronological order of publication.
Showing most recent 5 out of 24 [View All]
Farber B., Freitag D., Habash N., Rambow O. Improving NER in Arabic using a morphological tagger, in Proceedings of LREC 08, 2008. [Details]
Freitag D., Khadivi S. A sequence alignment model based on the averaged perceptron, in Proceedings of EMNLP 07, 2007. [Details]
Freitag D. Morphology induction from term clusters, in Proceedings of CoNLL 05, 2005. [Details]
Freitag D., Blume M., Byrnes J., Chow E., Kapadia S., Rohwer R., Wang Z. New experiments in distributional representations of synonymy, in Proceedings of CoNLL 05, 2005. [Details]
Freitag D. Toward Unsupervised Whole-Corpus Tagging, in Proceedings of Coling 2004, 2004. [Details]