Identification of comment-on sentences in online biomedical documents using support vector machines
In‐Cheol Kim, Daniel X. Le, George R. Thoma · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007
MEDLINE ® is the premier bibliographic online database of the National Library of Medicine, containing approximately 14 million citations and abstracts from over 4,800 biomedical journals. This paper presents an automated method based on support vector machines to identify a “comment-on ” list, which is a field in a MEDLINE citation denoting previously published articles commented on by a given article. For comparative study, we also introduce another method based on scoring functions that estimate the significance of each sentence in a given article. Preliminary experiments conducted on HTML-formatted online biomedical documents collected from 24 different journal titles show that the support vector machine with polynomial kernel function performs best in terms of recall and F-measure rates.