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Author and Group Insight through Linguistic Expression (AGILE)

Award Information
Agency: Department of Defense
Branch: Navy
Contract: N00014-12-M-0206
Agency Tracking Number: N121-080-0306
Amount: $150,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N121-080
Solicitation Number: 2012.1
Timeline
Solicitation Year: 2012
Award Year: 2012
Award Start Date (Proposal Award Date): 2012-05-07
Award End Date (Contract End Date): 2013-08-05
Small Business Information
1235 South Clark Street Suite 400
Arlington, VA -
United States
DUNS: 036593457
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Peter David
 Senior Engineer
 (703) 414-5009
 peter.david@dac.us
Business Contact
 Kelly McClelland
Title: Vice President of Adminis
Phone: (703) 414-5024
Email: kelly.mcclelland@dac.us.com
Research Institution
N/A
Abstract

The intelligence value of a document goes far beyond the face value of its content1. Clues to the identity, worldview, and even the psychological state of its author are encoded in features such as word choice, sentence structure, and explicit and implied statements of group membership. Years of research have shown that statistical and linguistic methods can shed light on a substantial amount of information about the identity and characteristics of an author. But traditional analysis techniques have been investigated in isolation, on a small scale, and with limited variety in the target documents. The Author and Group Insight through Linguistic Expression (AGILE) approach to author analysis extends DAC"s text analytics platform by incorporating a variety of extensions to the standard set of stylometric features used to attribute authorship. AGILE uses DAC"s existing semantic and sentiment processing technology to extract discourse-based features that capture the way authors perceive themselves and their relationships with other entities. The Phase I effort demonstrates how discourse features can be extracted from a variety of on-line sources of English and Arabic text. A series of experiments evaluates the power of discourse features to cluster documents and authors according to their social identity and world view.

* Information listed above is at the time of submission. *

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