Mining for proposal reviewers: lessons learned at the national science foundation

  • Authors:
  • Seth Hettich;Michael J. Pazzani

  • Affiliations:
  • Google, Inc., Mountain View, CA;Rutgers University, Piscataway, NJ

  • Venue:
  • Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper, we discuss a prototype application deployed at the U.S. National Science Foundation for assisting program directors in identifying reviewers for proposals. The application helps program directors sort proposals into panels and find reviewers for proposals. To accomplish these tasks, it extracts information from the full text of proposals both to learn about the topics of proposals and the expertise of reviewers. We discuss a variety of alternatives that were explored, the solution that was implemented, and the experience in using the solution within the workflow of NSF.