Translator Disclaimer
28 January 2008 Extracting a sparsely located named entity from online HTML medical articles using support vector machine
Author Affiliations +
Abstract
We describe a statistical machine learning method for extracting databank accession numbers (DANs) from online medical journal articles. Because the DANs are sparsely-located in the articles, we take a hierarchical approach. The HTML journal articles are first segmented into zones according to text and geometric features. The zones are then classified as DAN zones or other zones by an SVM classifier. A set of heuristic rules are applied on the candidate DAN zones to extract DANs according to their edit distances to the DAN formats. An evaluation shows that the proposed method can achieve a very high recall rate (above 99%) and a significantly better precision rate compared to extraction through brute force regular expression matching.
© (2008) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jie Zou, Daniel Le, and George R. Thoma "Extracting a sparsely located named entity from online HTML medical articles using support vector machine", Proc. SPIE 6815, Document Recognition and Retrieval XV, 68150P (28 January 2008); https://doi.org/10.1117/12.765907
PROCEEDINGS
10 PAGES


SHARE
Advertisement
Advertisement
Back to Top