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LANGUAGE COMPUTER CORPORATION SBIR Phase II Award, August 2018

A SBIR Phase II contract was awarded to Language Computer Corporation in August, 2018 for $1,499,864.0 USD from the U.S. Department of Defense and DARPA.

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AbstractTimelineTable: Further ResourcesReferences
sbir.gov/node/1592751
Is a
SBIR/STTR Awards
SBIR/STTR Awards

SBIR/STTR Award attributes

SBIR/STTR Award Recipient
‌
Language Computer Corporation
1
Government Agency
U.S. Department of Defense
U.S. Department of Defense
1
Government Branch
DARPA
DARPA
1
Award Type
SBIR1
Contract Number (US Government)
W31P4Q-18-C-00721
Award Phase
Phase II1
Award Amount (USD)
1,499,8641
Date Awarded
August 3, 2018
1
End Date
November 2, 2020
1
Abstract

In Phase II of AQUIRE, we will develop a prototype document background generation and management system that can be used by analysts and decision makers to access information gathered from a wide variety of unstructured sources about entities, relations, and complex situations. This system will be able to (1) construct topically organized entity corpora which are automatically supplemented with structured tables of important entity attributes and relations; (2) construct relation-focused document collections which contain fine-grained representations of the type of relation, with particular focus on identifying influential group members and sentiment towards other entities; (3) construct situation-focused information on complex events and other spatiotemporally grounded topics; and (4) provide added benefit to analysts in the form of hierarchical content exploration, hyperlinked access to and between information sources, and recommendations of other pertinent knowledge resources. System development during the option period will enable (5) deeper representations of the beliefs and goals of individuals and groups which drive their actions and responses; and (6) continuous updates from streaming data sources to identify new candidates for report generation (e.g., emerging events), find new information for inclusion in or revision of existing documents, and model document confidence and source trustworthiness through an unobtrusive feedback mechanism.

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