As America fights terrorism through its homeland security efforts, the capacity of its intelligence agencies to capture and generate information has far exceeded their ability to analyze it. Intelligence information in the form of text documents floods intelligence and other government agencies. These electronic documents exist in many forms, including cables, field analyst reports, all-caps message traffic, ship manifests, emails, open source documents and other data feeds. Until now, automated text analysis systems have required significant human intervention and have been limited to coding, indexing, keyword search or simple entity extractions that are inadequate because of imprecise and unpredictable results. Furthermore, these systems have been unable to fully leverage the breadth of analytic tools designed for dealing with structured information.

The Attensity Intelligence Analysis solution provides the capability to automate the extraction of "relational events" from free-form text - not only who or what, but why, when, where and how. The solution enables analytical processing by automating the transformation of written language into structured, relational data. The result is dramatically faster and more comprehensive detection of trends, anomalies, patterns and linkages, and more time for analysts to respond to them. Once extracted in this structured form, the information can be pushed downstream to feed virtually any system that processes relational tables. Such tabular data can be stored in a shared data warehouse repository for data mining or link analysis purposes; it can be input directly to a link analysis tool such as Visual Analytics' Visual Links, i2's Analyst's Notebook or other analytic applications; or it could be sent via an automated email alert to an analyst.

• Patented Extraction Engines extract entities and facts from intelligence documents providing both entities and the who, what, where, when, why and how
• Discovery applications enable investigators to dig through data to find issues without having to define them prior to discovery
• Analytic applications look across documents to uncover trends and issues and include visualizations to identify issues that are beyond the ordinary
• Alerts let investigators know when suspicious patterns arise

• Replaces the slow, expensive, error-prone task of manual document analysis and tagging
• Generates tabular, analysis-ready data structures from text sources up to 20,000 times faster than a human analyst
• Provides robust handling of "noisy" input, such as poor grammar, misspelled words, shorthand and unknown terms
• Integrates easily with existing data sources and targets