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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Evaluation of chemical and gene/protein entity recognition systems at BioCreative V.5: the CEMP and GPRO patents tracks Pérez-Pérez, Martin; Rabal, Obdulia; Pérez-Rodríguez, Gael; Vazquez, Miguel; Fdez-Riverola, Florentino; Oyarzabal, Julen; Valencia, Alfonso; Lourenço, Anália; Krallinger, Martin. - 2017 Abstract: This paper presents the results of the BioCreative V.5 offline tasks related to the evaluation of the performance as well as assess progress made by strategies used for the automatic recognition of mentions of chemical names and gene in running text of medicinal chemistry patent abstracts. A total of 21 teams submitted results for at least one of these tasks. The CEMP (chemical entity mention in patents) task entailed the detection of chemical named entity mentions. A total of 14 teams submitted 56 runs. The top performing team reached an F-score of 0.90 with a precision of 0.88 and a recall of 0.93. The GPRO (gene and protein related object) task focused on the detection of mentions of gene and protein related objects. The 7 participating teams (30 runs) had to detect gene/protein mentions that could be linked to at least one biological database, such as SwissProt or EntrezGene. The best F-score, recall and precision in this task were of 0.79, 0.83 and 0.77, respectively. The CEMP and GPRO gold standard corpora included training sets of 21,000 records and test sets of 9,000 records. Similar to the previous BioCreative CHEMDNER tasks, evaluation was based on micro-averaged F-score. The BeCalm platform supported prediction submission and evaluation (http://www.becalm.eu). ; We acknowledge the OpenMinted (654021) and the ELIXIREXCELERATE (676559) H2020 projects, and the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology for funding. The Spanish National Bioinformatics Institute (INB) unit at the Spanish National Cancer Research Centre (CNIO) is a member of the INB, PRB2-ISCIII and is supported by grant PT13/0001/0030, of the PE I+D+i 2013-2016, funded by ISCIII and ERDF. ; info:eu-repo/semantics/publishedVersion Keyword: BioCreative; CEMP; Chemical compounds; ChemNLP; Genes/proteins; GPRO; Named Entity Recognition; Text Mining URL: http://hdl.handle.net/1822/47885 BASE Hide details 2 The CHEMDNER corpus of chemicals and drugs and its annotation principles Krallinger, Martin; Rabal, Obdulia; Leitner, Florian. - : BioMed Central, 2015 BASE Show details 3 CHEMDNER: The drugs and chemical names extraction challenge Krallinger, Martin; Leitner, Florian; Rabal, Obdulia. - : BioMed Central, 2015 BASE Show details

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