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Table 3 Combined performance results

From: Overview of BioCreative II gene mention recognition

Exp Method P r F signif % alt
A CRF noalt, nom and word 0.9255 0.8885 0.9066 1-19, C-F 13.62
B BDT nom and word 0.9221 0.8885 0.9050 1-19, C-F 25.67
C BDT nom and word, top 10 teams 0.9118 0.8768 0.8940 1-19, E, F 23.37
D BDT nom only 0.9092 0.8773 0.8929 1-19, E, F 25.42
E BDT noalt, nom and word 0.9242 0.8165 0.8670 7-19, F 9.58
F BDT word only 0.7165 0.6187 0.6640 18-19 37.07
  1. The precision, recall, and F score of machine learning experiments to learn gene mentions using the data extracted from all submitted runs as features. Method column: BDT, boosted decision trees; CRF, conditional random fields; nom, all nomination features; word, words of candidate; noalt, alternate gene data not used. The column signif indicates the ranks of runs for which there was a significant difference, and the letters indicate the machine learning experiments for which there was a significant difference. The column % alt gives the percentage of alternate gene mentions among the resulting true positives.
{"cc_re__cc_re__cc_by_2_0": [" <meta name=\"dc.title\" content=\"Overview of BioCreative II gene mention recognition\"/>\n\n    <meta name=\"dc.source\" content=\"Genome Biology 2008 9:2\"/>\n\n    <meta name=\"dc.format\" content=\"text/html\"/>\n\n    <meta name=\"dc.publisher\" content=\"BioMed Central\"/>\n\n    <meta name=\"dc.date\" content=\"2008-09-01\"/>\n\n    <meta name=\"dc.type\" content=\"OriginalPaper\"/>\n\n    <meta name=\"dc.language\" content=\"En\"/>\n\n    <meta name=\"dc.copyright\" content=\"2008 Smith et al; licensee BioMed Central Ltd.\"/>\n\n    <meta name=\"dc.rights\" content=\"http://creativecommons.org/licenses/by/2.0\"/>\n\n    <meta name=\"dc.rightsAgent\" content=\"reprints@biomedcentral.com\"/>\n\n    <meta name=\"dc.description\" content=\"Nineteen teams presented results for the Gene Mention Task at the BioCreative II Workshop. In this task participants designed systems to identify substrings in sentences corresponding to gene name mentions. A variety of different methods were used and the results varied with a highest achieved F1 score of 0.8721. Here we present brief descriptions of all the methods used and a stati"], "copyright__copyright__copyright": ["om all submitted runs as features. Method column: BDT, boosted decision trees; CRF, conditional random fields; nom, all nomination features; word, words of candidate; noalt, alternate gene data not used. The column signif indicates the ranks of runs for which there was a significant difference, and the letters indicate the machine learning experiments for which there was a significant difference. The column % alt gives the percentage of alternate gene mentions among the resulting true positives.", "om all submitted runs as features. Method column: BDT, boosted decision trees; CRF, conditional random fields; nom, all nomination features; word, words of candidate; noalt, alternate gene data not used. The column signif indicates the ranks of runs for which there was a significant difference, and the letters indicate the machine learning experiments for which there was a significant difference. The column % alt gives the percentage of alternate gene mentions among the resulting true positives.", "om all submitted runs as features. Method column: BDT, boosted decision trees; CRF, conditional random fields; nom, all nomination features; word, words of candidate; noalt, alternate gene data not used. The column signif indicates the ranks of runs for which there was a significant difference, and the letters indicate the machine learning experiments for which there was a significant difference. The column % alt gives the percentage of alternate gene mentions among the resulting true positives.", "om all submitted runs as features. Method column: BDT, boosted decision trees; CRF, conditional random fields; nom, all nomination features; word, words of candidate; noalt, alternate gene data not used. The column signif indicates the ranks of runs for which there was a significant difference, and the letters indicate the machine learning experiments for which there was a significant difference. The column % alt gives the percentage of alternate gene mentions among the resulting true positives."]}