NDLI: A Structural SVM Approach for Reference Parsing

Content Provider	IEEE Xplore Digital Library
Author	Xiaoli Zhang Jie Zou Le, D.X. Thoma, G.R.
Copyright Year	2010
Abstract	MEDLINE®, the flagship database of the U.S. National Library of Medicine, is a critical source of information for biomedical research and clinical medicine. The automated extraction of bibliographic data, such as article titles, author names, abstracts, and references, is essential to the affordable creation of this citation database. References, typically appearing at the end of journal articles, can provide valuable information for extracting Comment-On/Comment-In data (identifying commentary article pairs) and assigning MeSH terms in an article. The regular structure in references enables us to implement structural SVM, a newly developed structured learning algorithm to parse references. In this study we use two types of contextual features to compare structural SVM with conventional SVM. When only basic observation features are used for each token, structural SVM achieves higher performance compared to SVM since it utilizes the contextual label features. However, when the contextual observation features from neighboring tokens are combined, SVM performance improves greatly, and is close to that of structural SVM after adding the second order contextual observation features. Both methods achieve above 98% token classification accuracy and above 95% overall chunk-level accuracy for reference parsing.
Starting Page	479
Ending Page	484
File Size	285264
Page Count	6
File Format	PDF
ISBN	9781424492114
DOI	10.1109/ICMLA.2010.77
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training MEDLINE reference parsing Accuracy contextual features Hidden Markov models stuctural SVM Feature extraction Libraries Data mining
Content Type	Text
Resource Type	Article

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