SBIR Phase I:Building a Flexible, Technology Adaptive Architecture to Support Processing of Content by Knowledge Workers

Award Information
Agency:
National Science Foundation
Branch
n/a
Amount:
$150,000.00
Award Year:
2010
Program:
SBIR
Phase:
Phase I
Contract:
1013935
Award Id:
99102
Agency Tracking Number:
1013935
Solicitation Year:
n/a
Solicitation Topic Code:
IC1
Solicitation Number:
n/a
Small Business Information
3604 Vale Station Rd, Oakton, VA, 22124
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
145858168
Principal Investigator:
EricKoefoot
PhD
(703) 472-8337
ekoefoot@yahoo.com
Business Contact:
EricKoefoot
PhD
(703) 472-8337
ekoefoot@yahoo.com
Research Institute:
n/a
Abstract
This Small Business Innovation Research (SBIR) Phase I Project addresses the gap between the capabilities of today's natural language processing systems and the accuracy requirements of knowledge workers (analysts and researchers) in language-sensitive fields such as public relations, foreign affairs, and crisis management. Knowledge workers in many organizations monitor and analyze print and web coverage for content of interest. When the volume of search results is large, some filter, classify and score the results with Natural Language Processing (NLP) systems using complex libraries of words, patterns, and context-specific algorithms. However, users complain that these systems fall short of desired accuracy, missing rhetorical devices such as irony, sarcasm, metaphors, double entendre, and improperly interpreting references. Users with high thresholds for accuracy thus turn to manual processes to either supplement or substitute for technology. This project will test a prototype architecture allowing rapid insertion, testing, and adaptation of text analysis algorithms and a workflow process efficiently integrating human review judgment. Once commercialized, the system will enable more rapid adoption of technology by knowledge workers. In fields with high accuracy requirements, the need for human judgment has constrained technology use to discrete areas like search, while in subsequent processing steps, analysts must manually capture, classify, score, analyze, and report on the output.

* information listed above is at the time of submission.

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