A stacked sequential learning method for investigator name recognition from web-based medical articles

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

doi:10.1117/12.839141

18 January 2010 A stacked sequential learning method for investigator name recognition from web-based medical articles

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

Proceedings Volume 7534, Document Recognition and Retrieval XVII; 753404 (2010) https://doi.org/10.1117/12.839141
Event: IS&T/SPIE Electronic Imaging, 2010, San Jose, California, United States

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.

Citation Download Citation

Xiaoli Zhang, Jie Zou, Daniel X. Le, and George Thoma "A stacked sequential learning method for investigator name recognition from web-based medical articles", Proc. SPIE 7534, Document Recognition and Retrieval XVII, 753404 (18 January 2010); https://doi.org/10.1117/12.839141

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