Automatic metadata generation & evaluation

  • Authors:

  • Elizabeth D. Liddy;Eileen Allen;Sarah Harwell;Susan Corieri;Ozgur Yilmazel;N. Ercan Ozgencil;Anne Diekema;Nancy McCracken;Joanne Silverstein;Stuart Sutton

  • Affiliations:

  • School of Information Studies, Syracuse University;School of Information Studies, Syracuse University;School of Information Studies, Syracuse University;School of Information Studies, Syracuse University;School of Information Studies, Syracuse University;School of Information Studies, Syracuse University;School of Information Studies, Syracuse University;School of Information Studies, Syracuse University;Information Institute, Syracuse University, Syracuse, NY;University of Washington, Seattle, WA

  • Venue:
  • SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

The poster reports on a project in which we are investigating methods for breaking the human metadata-generation bottleneck that plagues Digital Libraries. The research question is whether metadata elements and values can be automatically generated from the content of educational resources, and correctly assigned to mathematics and science educational materials. Natural Language Processing and Machine Learning techniques were implemented to automatically assign values of the GEMgenerate metadata element set tofor learning resources provided by the Gateway for Education (GEM), a service that offers web access to a wide range of educational materials. In a user study, education professionals evaluated the metadata assigned to learning resources by either automatic tagging or manual assignment. Results show minimal difference in the eyes of the evaluators between automatically generated metadata and manually assigned metadata.