EXTRACTION OF GENE-DISEASE RELATIONS FROM MEDLINE USING DOMAIN DICTIONARIES AND MACHINE LEARNING

HONG-WOO CHUN, YOSHIMASA TSURUOKA, JIN-DONG KIM, RIE SHIBA, NAOKI NAGATA, TERUYOSHI HISHIKI, JUN'ICHI TSUJII
<span title="">2005</span> <i title="WORLD SCIENTIFIC"> Biocomputing 2006 </i> &nbsp;
We describe a system that extracts disease-gene relations from M edLine. We constructed a dictionary for disease and gene names from six public databases and extracted relation candidates by dictionary matching. Since dictionary matching produces a large number of false positives, we developed a method of machine learning-based named entity recognition (NER) to filter out false recognitions of disease/gene names. We found that the performance of relation extraction is heavily dependent upon the
more &raquo; ... performance of NER filtering and that the filtering improves the precision of relation extraction by 26.7% at the cost of a small reduction in recall.
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