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Automatic metadata generation provides scalability and usability for digital libraries and their collections. Machine learning methods offer robust and adaptable automatic metadata extraction. We describe a Support Vector Machine classification-based method for metadata extraction from header part of research papers and show that it outperforms other machine learning methods on the same task. The method first classifies each line of the header into one or more of 15 classes. An iterativedoi:10.1109/jcdl.2003.1204842 dblp:conf/jcdl/HanGMZZF03 fatcat:jrrwk3uixzcgfdsevx2cb3iqga