Combining discriminative SVM models for the improved recognition of investigator names in medical articles

Xiaoli Zhang; Jie Zou; Daniel X. Le; George R. Thoma

doi:10.1117/12.2007336

4 February 2013 Combining discriminative SVM models for the improved recognition of investigator names in medical articles

Xiaoli Zhang, Jie Zou, Daniel X. Le, George R. Thoma

Author Affiliations +

Proceedings Volume 8658, Document Recognition and Retrieval XX; 865815 (2013) https://doi.org/10.1117/12.2007336
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States

Abstract

Investigators are people who are listed as members of corporate organizations but not entered as authors in an article. Beginning with journals published in 2008, investigator names are required to be included in a new bibliographic field in MEDLINE citations. Automatic extraction of investigator names is necessary due to the increase in collaborative biomedical research and consequently the large number of such names. We implemented two discriminative SVM models, i.e., SVM and structural SVM, to identify named entities such as the first and last names of investigators from online medical journal articles. Both approaches achieve good performance at the word and name chunk levels. We further conducted an error analysis and found that SVM and structural SVM can offer complementary information about the patterns to be classified. Hence, we combined the two independently trained classifiers where the SVM is chosen as a base learner with its outputs enhanced by the predictions from the structural SVM. The overall performance especially the recall rate of investigator name retrieval exceeds that of the standalone SVM model.

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Xiaoli Zhang, Jie Zou, Daniel X. Le, and George R. Thoma "Combining discriminative SVM models for the improved recognition of investigator names in medical articles ", Proc. SPIE 8658, Document Recognition and Retrieval XX, 865815 (4 February 2013); https://doi.org/10.1117/12.2007336

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