BACKGROUND: Previously, gene normalization (GN) systems are mostly focused on disambiguation using contextual information. An effective gene mention tagger is deemed unnecessary because the subsequent steps will filter out false positives and high recall is sufficient. However, unlike similar tasks in the past BioCreative challenges, the BioCreative III GN task is particularly challenging because it is not species-specific. Required to process full-length articles, an ineffective gene mention tagger may produce a huge number of ambiguous false positives that overwhelm subsequent filtering steps while still missing many true positives. RESULTS: We present our GN system participated in the BioCreative III GN task. Our system applies a typical 2-stage approach to GN but features a soft tagging gene mention tagger that generates a set of overlapping gene mention variants with a nearly perfect recall. The overlapping gene mention variants increase the chance of precise match in the dictionary and alleviate the need of disambiguation. Our GN system achieved a precision of 0.9 (F-score 0.63) on the BioCreative III GN test corpus with the silver annotation of 507 articles. Its TAP-k scores are competitive to the best results among all participants. CONCLUSIONS: We show that despite the lack of clever disambiguation in our gene normalization system, effective soft tagging of gene mention variants can indeed contribute to performance in cross-species and full-text gene normalization.
BACKGROUND: Previously, gene normalization (GN) systems are mostly focused on disambiguation using contextual information. An effective gene mention tagger is deemed unnecessary because the subsequent steps will filter out false positives and high recall is sufficient. However, unlike similar tasks in the past BioCreative challenges, the BioCreative III GN task is particularly challenging because it is not species-specific. Required to process full-length articles, an ineffective gene mention tagger may produce a huge number of ambiguous false positives that overwhelm subsequent filtering steps while still missing many true positives. RESULTS: We present our GN system participated in the BioCreative III GN task. Our system applies a typical 2-stage approach to GN but features a soft tagging gene mention tagger that generates a set of overlapping gene mention variants with a nearly perfect recall. The overlapping gene mention variants increase the chance of precise match in the dictionary and alleviate the need of disambiguation. Our GN system achieved a precision of 0.9 (F-score 0.63) on the BioCreative III GN test corpus with the silver annotation of 507 articles. Its TAP-k scores are competitive to the best results among all participants. CONCLUSIONS: We show that despite the lack of clever disambiguation in our gene normalization system, effective soft tagging of gene mention variants can indeed contribute to performance in cross-species and full-text gene normalization.
Authors: Alexander A Morgan; Lynette Hirschman; Marc Colosimo; Alexander S Yeh; Jeff B Colombe Journal: J Biomed Inform Date: 2004-12 Impact factor: 6.317
Authors: Alexander A Morgan; Zhiyong Lu; Xinglong Wang; Aaron M Cohen; Juliane Fluck; Patrick Ruch; Anna Divoli; Katrin Fundel; Robert Leaman; Jörg Hakenberg; Chengjie Sun; Heng-hui Liu; Rafael Torres; Michael Krauthammer; William W Lau; Hongfang Liu; Chun-Nan Hsu; Martijn Schuemie; K Bretonnel Cohen; Lynette Hirschman Journal: Genome Biol Date: 2008-09-01 Impact factor: 13.583
Authors: Larry Smith; Lorraine K Tanabe; Rie Johnson nee Ando; Cheng-Ju Kuo; I-Fang Chung; Chun-Nan Hsu; Yu-Shi Lin; Roman Klinger; Christoph M Friedrich; Kuzman Ganchev; Manabu Torii; Hongfang Liu; Barry Haddow; Craig A Struble; Richard J Povinelli; Andreas Vlachos; William A Baumgartner; Lawrence Hunter; Bob Carpenter; Richard Tzong-Han Tsai; Hong-Jie Dai; Feng Liu; Yifei Chen; Chengjie Sun; Sophia Katrenko; Pieter Adriaans; Christian Blaschke; Rafael Torres; Mariana Neves; Preslav Nakov; Anna Divoli; Manuel Maña-López; Jacinto Mata; W John Wilbur Journal: Genome Biol Date: 2008-09-01 Impact factor: 13.583