Inferring the semantics hidden in big heterogeneous data
Abstract
The ultimate goal of this project is to develop a class of inference algorithms that enable to explore and discover hidden structures (semantics) from massive text collections, and that enable to do accurate predictions in practical applications. To this end, the PI focuses on developing 1) Provably fast inference methods for topic models. The methods must have theoretical guarantees on quality and are general enough to be employed in other contexts, 2) Online algorithms that can learn hidden structures (semantics) from streaming/dynamic text collections. Also they can easily handle millions/billions of heterogeneous texts. The algorithms should exploit well parallel/distributed computing environments if available, and 3) Effective methods for practical applications including question answering, recommendation, and social network analysis. Those methods should enclose our inference methods as an internal step.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 23, 2016
- Source ID
- FA23861514011
Entities
People
- Khoat Than
Organizations
- Air Force Office of Scientific Research
- Hanoi University of Science and Technology
- United States Air Force