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

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. Categories and Subject Descriptors H.2.8 [Database Applications]: Data Mining General Terms Algorithms, Human Factors, Emerging applications, technology, and issues Keywords Keyword extraction, similarity functions, clustering, information retrieval.

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Seth Hettich, Michael J. Pazzani
• Keywords: Data Mining, Directors Sort Proposals, KDD 2006, Keywords Keyword Extraction, Program Directors