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Enhanced Text Analytics Using Lifted Probabilistic Inference Algorithms

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-15-C-0003
Agency Tracking Number: F13A-T11-0017
Amount: $744,269.00
Phase: Phase II
Program: STTR
Solicitation Topic Code: AF13-AT11
Solicitation Number: 2013.1
Solicitation Year: 2013
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-01-08
Award End Date (Contract End Date): 2017-01-07
Small Business Information
180 2nd Street Suite B2
Los Altos, CA 94022
United States
DUNS: 10887441
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Homa Yazdani
 (650) 996-1810
Business Contact
 patrick Barkhordarian
Phone: (415) 595-7615
Research Institution
 Wake Forest University
 Dr. Sriraam Natarajan
School of Bio-Medical Engineering
Winston Salem, NC 27157
United States

 (336) 716-8430
 Nonprofit College or University

ABSTRACT: LVI proposes developing an advanced framework of lifted probabilistic inference algorithms for enhancing the scaling and accuracy of text analytics. In Phase I, LVI explored the scalability of various lifted inference techniques for utilizing Markov Logic Networks (MLN) in the Tuffy software package. Phase I also included investigation and demonstration of DeepDive, a scalable, high-performance inference and learning engine for text analytics. These techniques were applied for automated knowledge base construction from free text, using abductive reasoning for optimal updates to the knowledge base. The MLN used unsupervised joint inferencing to combine record segmentation, co-reference resolution, and entity resolution in a single process, as opposed to a pipelined approach. The Phase II end-to-end prototype will be developed to perform text analytics over an information repository using the optimized joint inference technique. The prototype capabilities including joint inference over cross-document and multiple knowledge bases will be demonstrated through, for example, answering specific queries without considering the entire model and/or the entire evidence. "Distance supervision" and the Stanford Dependency Parser for NLP will be used to leverage external data sources for entity identification. Collaborating with a large financial institution MLN will be developed for entity recognition, relationship discovery and classification. ; BENEFIT: The algorithms for Lifted inference have a variety of applications. They include Social networks, object recognition, link prediction, activity recognition, model counting, bio-medical applications and relational reasoning and learning. Fundamental building block to improve current capabilities.

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

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