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Proceedings Paper

A stacked sequential learning method for investigator name recognition from web-based medical articles
Author(s): Xiaoli Zhang; Jie Zou; Daniel X. Le; George Thoma
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Paper Abstract

"Investigator Names" is a newly required field in MEDLINE citations. It consists of personal names listed as members of corporate organizations in an article. Extracting investigator names automatically is necessary because of the increasing volume of articles reporting collaborative biomedical research in which a large number of investigators participate. In this paper, we present an SVM-based stacked sequential learning method in a novel application - recognizing named entities such as the first and last names of investigators from online medical journal articles. Stacked sequential learning is a meta-learning algorithm which can boost any base learner. It exploits contextual information by adding the predicted labels of the surrounding tokens as features. We apply this method to tag words in text paragraphs containing investigator names, and demonstrate that stacked sequential learning improves the performance of a nonsequential base learner such as an SVM classifier.

Paper Details

Date Published: 18 January 2010
PDF: 7 pages
Proc. SPIE 7534, Document Recognition and Retrieval XVII, 753404 (18 January 2010); doi: 10.1117/12.839141
Show Author Affiliations
Xiaoli Zhang, National Library of Medicine (United States)
Jie Zou, National Library of Medicine (United States)
Daniel X. Le, National Library of Medicine (United States)
George Thoma, National Library of Medicine (United States)

Published in SPIE Proceedings Vol. 7534:
Document Recognition and Retrieval XVII
Laurence Likforman-Sulem; Gady Agam, Editor(s)

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