Text mining for indexing

Authors:
Judith Gelernter;Michael Lesk
Affiliations:
Carnegie-Mellon University, Pittsburgh, PA, USA;Rutgers University, New Brunswick, NJ, USA
Venue:
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Year:
2009

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Abstract

This paper describes techniques for automatically extracting and classifying maps found within articles. The process uses image analysis to find text in maps, document structure to find captions and titles, and then text mining to assign each map to a subject category, a geographical place, and a time period. The text analysis is based on authority lists taken from gazetteers and from library classifications.