2011
DOI: 10.1186/1471-2105-12-s8-s2
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The gene normalization task in BioCreative III

Abstract: BackgroundWe report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an Expectation Maximization (EM) algorithm appr… Show more

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Cited by 104 publications
(101 citation statements)
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“…(Tsuruoka et al, 2007;Ristad and Yianilos, 1998;Lu et al, 2011;McCallum et al, 2012). For example, McCallum et al (2012) used conditional random field to learn edit distance between phrases.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…(Tsuruoka et al, 2007;Ristad and Yianilos, 1998;Lu et al, 2011;McCallum et al, 2012). For example, McCallum et al (2012) used conditional random field to learn edit distance between phrases.…”
Section: Related Workmentioning
confidence: 99%
“…Traditional approaches, e.g. (Ristad and Yianilos, 1998;Aronson, 2001;Lu et al, 2011;McCallum et al, 2012), used proximity matching or heuristic string matching rules based on dictionary lookup when mapping texts to medical concepts. For example, Ristad and Yianilos (1998) incorporated edit-distance when mapping similar texts.…”
Section: Introductionmentioning
confidence: 99%
“…pathways, proteins, etc) and extracting the relationships with natural language processing methods. Other text mining software suites specifically designed for biomedical data analytics are reviewed in (Lu et al, 2011).…”
Section: Text Mining Approachesmentioning
confidence: 99%
“…Automated text mining of these data has been already applied to derive gene-disease associations (e.g. [12,13]); however, these approaches require recognizing gene names using automated text mining methods that suffer from low accuracy [14,15]. Another way to extract gene-disease associations from the literature is to integrate the numerous manually curated annotations of PubMed citations for genes or diseases.…”
Section: Introductionmentioning
confidence: 99%