all-SSC outperforms participants on GSC

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CALBC workshop II Mar 16, 2011, EBI Partners & Participants Confusion Matrix, exact matching Reference set Assessment PGN DISO SPE CHEM PGN 412,866 2,673 395 106,560 DISO 451,175 4,024 2,126 SPE 474,453 912 CHEM 414,798 Confusion Matrix, nested matching Reference set Assessment PGN DISO SPE CHEM PGN 412,866 2,927 695 113,546 DISO 3,055 451,175 7,910 2,826 SPE 516 7,992 474,453 1,376 CHEM 107,436 2,859 1,155 414,798 14
CALBC workshop II Mar 16, 2011, EBI Stakes � Corpus alignment is doable: different semantic types, single document � Different similarity measures for harmonisation: exact, nested, cos. � <strong>SSC</strong> generation scales / generalisable: Iterative generation � Partner-<strong>SSC</strong> <strong>outperforms</strong> partners on GSC � <strong>all</strong>-<strong>SSC</strong> <strong>outperforms</strong> <strong>participants</strong> on GSC � <strong>SSC</strong> is the best option when no GSC available � Incremental improvement of <strong>SSC</strong> performance: Large number of contributions leads to higher performance � Performance of the <strong>SSC</strong> concerning the other types � Normalisation of the <strong>SSC</strong>: concept id annotations for mentions � Use cases of the <strong>SSC</strong> 15
Page 1 and 2: Challenge I vs. Ch
Page 3 and 4: CALBC workshop II Mar 16, 2011, EBI
Page 5 and 6: 2nd Challenge Roll
Page 7 and 8: Timeline of the CALBC project 15 <s
Page 9 and 10: Solution Dictionary-based concept r
Page 11 and 12: F-Meas 90% 80% 70% 60% 50% 40% 30%
Page 13: CALBC workshop II Mar 16, 2011, EBI

all-SSC outperforms participants on GSC

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