Reasoning Efficiently From Self-Organization of Unstructured Data (Resound)
Final technical rept. Aug 2006-Sep 2009
HNC SOFTWARE INC SAN DIEGO CA
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During the two years since its effective start date 28 Aug 2006, the HNC IARPA CASE project has brought us closer to the goal of a universal and optimal approach to information extraction. Building on the earlier IARPA NIMD project, new algorithms were developed for unsupervised learning of hierarchical feature sets for text and imagery, and the Text Analysis Engine TAE SOA component of the CASE Integrated Architecture was extended to several languages and more thoroughly hardened and tested. Most importantly, we have clarified our understanding of universal abstract principles that can guide future research on information extraction and organization directly to its greatest potential payoff.
- Information Science
- Numerical Mathematics