Reasoning Efficiently From Self-Organization of Unstructured Data (Resound)

Abstract

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.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2009
Accession Number
ADA510848

Entities

People

  • Richard Rohwer

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Computer Languages
  • Computer Vision
  • Information Processing
  • Information Science
  • Language
  • Machine Learning
  • Ontologies
  • Reasoning
  • Self Organizing Systems
  • Statistical Algorithms
  • Statistical Analysis
  • Supervised Machine Learning
  • Target Recognition
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Neural Network Machine Learning.
  • Software Engineering.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Machine Translation