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Representation and Inference for Developing Deep Language Engines (RIDDLE)

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-14-C-0021
Agency Tracking Number: F13A-T11-0026
Amount: $149,991.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF13-AT11
Solicitation Number: 2013.A
Timeline
Solicitation Year: 2013
Award Year: 2014
Award Start Date (Proposal Award Date): 2013-10-22
Award End Date (Contract End Date): 2014-07-21
Small Business Information
625 Mount Auburn Street
Cambridge, MA -
United States
DUNS: 115243701
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Avi Pfeffer
 Principal Scientist
 (617) 491-3474
 apfeffer@cra.com
Business Contact
 Mark Felix
Title: Contracts Manager
Phone: (617) 491-3474
Email: mfelix@cra.com
Research Institution
 University of Texas at Dallas
 Dina Caplinger
 
800 W. Campbell Road
Richardson, TX 75080-
United States

 (972) 883-2312
 Nonprofit college or university
Abstract

ABSTRACT: Intelligence analysts need to process large amounts of text information to form an understanding of a topic of interest. The sheer amount of information can be overwhelming, so automated text analytics that assist with filtering, information extraction, and document understanding, can be highly beneficial. Deep natural language processing (NLP) applications require both structural knowledge of language and background knowledge of the domain. Statistical relational learning representations support reasoning about knowledge-rich domains under uncertainty, but joint inference in NLP applications is a challenging task due to the thousands of variables and millions of features. Charles River Analytics proposes to develop Representation and Inference for Developing Deep Language Engines (RIDDLE), which investigates advanced joint inference algorithms for NLP and the representational issues that are intimately tied to inference. In particular, we will develop three novel classes of inference algorithms, including both lifted and non-lifted algorithms, as well as structured representations of knowledge to support inference using probabilistic programming. We will perform a cross-cutting evaluation of representations and inference algorithms on a range of NLP tasks. BENEFIT: RIDDLE will benefit intelligence analysts by enabling them to filter and extract meaning from large numbers of text documents, thereby supporting more timely and effective intelligence. RIDDLE will also be beneficial to commercial applications of NLP systems, such as text analytics of medical databases. The algorithms developed under this effort will also extend to our commercial FigaroTM probabilistic modeling tool, enabling it to be applied to larger and richer domains.

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

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