Combining SVM classifiers to identify investigator name zones in biomedical articles

Jongwoo Kim; Daniel X. Le; George R. Thoma

doi:10.1117/12.910517

23 January 2012 Combining SVM classifiers to identify investigator name zones in biomedical articles

Jongwoo Kim, Daniel X. Le, George R. Thoma

Proceedings Volume 8297, Document Recognition and Retrieval XIX; 829704 (2012) https://doi.org/10.1117/12.910517
Event: IS&T/SPIE Electronic Imaging, 2012, Burlingame, California, United States

Abstract

This paper describes an automated system to label zones containing Investigator Names (IN) in biomedical articles, a key item in a MEDLINE^® citation. The correct identification of these zones is necessary for the subsequent extraction of IN from these zones. A hierarchical classification model is proposed using two Support Vector Machine (SVM) classifiers. The first classifier is used to identify an IN zone with highest confidence, and the other classifier identifies the remaining IN zones. Eight sets of word lists are collected to train and test the classifiers, each set containing collections of words ranging from 100 to 1,200. Experiments based on a test set of 105 journal articles show a Precision of 0.88, 0.97 Recall, 0.92 F-Measure, and 0.99 Accuracy.

Citation Download Citation

Jongwoo Kim, Daniel X. Le, and George R. Thoma "Combining SVM classifiers to identify investigator name zones in biomedical articles", Proc. SPIE 8297, Document Recognition and Retrieval XIX, 829704 (23 January 2012); https://doi.org/10.1117/12.910517

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