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10.1186/s12859-015-0564-6 http://scihub22266oqcxt.onion/10.1186/s12859-015-0564-6 C4450488!4450488!25925131     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       BMC+Bioinformatics 2015 ; 16 (ä): ä Nephropedia Template TP {{tp|p=25925131|t=2015. An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition |pdf=|usr=}}{{25925131}} gab.com Text An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=25925131 #FOAvip Background: This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. Results: The 2013 BioASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PubMed documents with MeSH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MeSH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BioASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. Conclusions: A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MeSH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PubMed Central; produce ?exact? and paragraph-sized ?ideal? answers (summaries). The results of the systems that participated in the 2013 BioASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM?s MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the ?ideal? answers; hence, they produced high quality summaries as answers. Overall, BioASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs. Electronic supplementary material: The online version of this article (doi:10.1186/s12859-015-0564-6) contains supplementary material, which is available to authorized users. Twit Text FOAVip An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=25925131 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25925131 #FOAvip English Wikipedia <ref>{{Cite pmid|25925131}}</ref> An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition #MMPMID25925131 Tsatsaronis G; Balikas G; Malakasiotis P; Partalas I; Zschunke M; Alvers MR; Weissenborn D; Krithara A; Petridis S; Polychronopoulos D; Almirantis Y; Pavlopoulos J; Baskiotis N; Gallinari P; Artiéres T; Ngomo ACN; Heino N; Gaussier E; Barrio-Alvers L; Schroeder M; Androutsopoulos I; Paliouras G BMC Bioinformatics 2015[]; 16 (ä): ä PMID25925131show ga Background: This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. Results: The 2013 BioASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PubMed documents with MeSH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MeSH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BioASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. Conclusions: A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MeSH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PubMed Central; produce ?exact? and paragraph-sized ?ideal? answers (summaries). The results of the systems that participated in the 2013 BioASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM?s MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the ?ideal? answers; hence, they produced high quality summaries as answers. Overall, BioASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs. Electronic supplementary material: The online version of this article (doi:10.1186/s12859-015-0564-6) contains supplementary material, which is available to authorized users. äAn overview of the BIOASQ large-scale biomedical semantic indexing and(epmc).pdf DeepDyve Pubget Overpricing