| Summary: | 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).
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