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Extracting a Sparsely-Located Named Entity from Online HTML Medical Articles Using Support Vector Machine
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.