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Semantic Parsing and role Labeling In Combination Effort (SPLICE)

Award Information
Agency: Department of Defense
Branch: Defense Threat Reduction Agency
Contract: HDTRA1-15-P-0027
Agency Tracking Number: T14B-003-0030
Amount: $150,000.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: DTRA14B-003
Solicitation Number: 2014.2
Timeline
Solicitation Year: 2014
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-09-21
Award End Date (Contract End Date): 2016-04-20
Small Business Information
1400 Crystal Drive Suite 1400
Arlington, VA 22202
United States
DUNS: 000000000
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Jennifer Sikos
 (703) 414-5010
 jennifer.sikos@dac.us
Business Contact
 Dana Ho
Phone: (703) 414-5016
Email: dana.ho@dac.us
Research Institution
 ICSI
 Theresa Hilaire
 
1947 Center Street Suite 600
Berkley, CA 94704
United States

 (510) 666-2911
 Domestic nonprofit research organization
Abstract

With missions surrounding counterproliferation, nonproliferation and WMD reduction, the stakes for DTRA are incredibly high. Effectively conducting this work involves cooperation and coordination across a wide range of military and government organizations throughout the United States and the world. The decentralization of threats has led to an explosion in the sources and types of data needed to identify, track, and mitigate these threats. Managing highly diverse information drawn from a wide range of agencies and open sources is exceptionally challenging. Even when this problem is restricted to unstructured text, there is simply too much data coming from too many sources for any analyst to appropriately handle all available information and effectively exploit the knowledge it contains with current NLP technologies. Under the Semantic Parsing and role Labeling In Combination Effort (SPLICE), DAC has partnered with the ICSI to build a Deep NLP capability that combines the strengths of Semantic Parsing with those of Semantic Role Labeling. The resulting Deep NLP capability will: 1) find links between entities, events and concepts that previously could only be found through manual analysis; and 2) support population of semantically structured knowledge bases with the confidence that all relevant available information can be fully exploited.

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

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