Automatic Metadata Generation & Evaluation

Authors

E. D. Liddy
E. Allen
S. Harwell
S. Corieri
O. Yilmazel
N. E. Ozgencil
Anne R. Diekama, Utah State UniversityFollow
N. McCracken
J. Silverstein
S. Sutton

Document Type

Poster

Journal/Book Title/Conference

Special Interest Group for Information Retrieval

Publisher

Association for Computing Machinery

Publication Date

2002

First Page

401

Last Page

402

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.

Comments

Originally published by the Association for Computing Machinery. Publisher's PDF available through remote link.

Recommended Citation

Liddy, E. D.; Allen, E.; Harwell, S.; Corieri, S.; Yilmazel, O.; Ozgencil, N. E.; Diekema, A.; McCracken, N.; Silverstein, J. and Sutton, S. Automatic Metadata Generation & Evaluation. In: Proceedings of the Special Interest Group for Information Retrieval. Tampere, Finland, 2002.