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10.1186/s13326-017-0123-3 http://scihub22266oqcxt.onion/10.1186/s13326-017-0123-3 C5392968!5392968!28412964     free     free     free Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 219.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Biomed+Semantics 2017 ; 8 (ä): ä Nephropedia Template TP {{tp|p=28412964|t=2017. MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank |pdf=|usr=}}{{28412964}} gab.com Text MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank JO: J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=28412964 #FOAvip Background: MeSH indexing is the task of assigning relevant MeSH terms based on a manual reading of scholarly publications by human indexers. The task is highly important for improving literature retrieval and many other scientific investigations in biomedical research. Unfortunately, given its manual nature, the process of MeSH indexing is both time-consuming (new articles are not immediately indexed until 2 or 3 months later) and costly (approximately ten dollars per article). In response, automatic indexing by computers has been previously proposed and attempted but remains challenging. In order to advance the state of the art in automatic MeSH indexing, a community-wide shared task called BioASQ was recently organized. Methods: We propose MeSH Now, an integrated approach that first uses multiple strategies to generate a combined list of candidate MeSH terms for a target article. Through a novel learning-to-rank framework, MeSH Now then ranks the list of candidate terms based on their relevance to the target article. Finally, MeSH Now selects the highest-ranked MeSH terms via a post-processing module. Results: We assessed MeSH Now on two separate benchmarking datasets using traditional precision, recall and F1-score metrics. In both evaluations, MeSH Now consistently achieved over 0.60 in F-score, ranging from 0.610 to 0.612. Furthermore, additional experiments show that MeSH Now can be optimized by parallel computing in order to process MEDLINE documents on a large scale. Conclusions: We conclude that MeSH Now is a robust approach with state-of-the-art performance for automatic MeSH indexing and that MeSH Now is capable of processing PubMed scale documents within a reasonable time frame. Availability: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/MeSHNow/. Twit Text FOAVip MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank ????J Biomed Semantics http://www.kidney.de/pget.php?&tw=1&pmid=28412964 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28412964 #FOAvip English Wikipedia <ref>{{Cite pmid|28412964}}</ref> MeSH Now: automatic MeSH indexing at PubMed scale via learning to rank #MMPMID28412964 Mao Y; Lu Z J Biomed Semantics 2017[]; 8 (ä): ä PMID28412964show ga Background: MeSH indexing is the task of assigning relevant MeSH terms based on a manual reading of scholarly publications by human indexers. The task is highly important for improving literature retrieval and many other scientific investigations in biomedical research. Unfortunately, given its manual nature, the process of MeSH indexing is both time-consuming (new articles are not immediately indexed until 2 or 3 months later) and costly (approximately ten dollars per article). In response, automatic indexing by computers has been previously proposed and attempted but remains challenging. In order to advance the state of the art in automatic MeSH indexing, a community-wide shared task called BioASQ was recently organized. Methods: We propose MeSH Now, an integrated approach that first uses multiple strategies to generate a combined list of candidate MeSH terms for a target article. Through a novel learning-to-rank framework, MeSH Now then ranks the list of candidate terms based on their relevance to the target article. Finally, MeSH Now selects the highest-ranked MeSH terms via a post-processing module. Results: We assessed MeSH Now on two separate benchmarking datasets using traditional precision, recall and F1-score metrics. In both evaluations, MeSH Now consistently achieved over 0.60 in F-score, ranging from 0.610 to 0.612. Furthermore, additional experiments show that MeSH Now can be optimized by parallel computing in order to process MEDLINE documents on a large scale. Conclusions: We conclude that MeSH Now is a robust approach with state-of-the-art performance for automatic MeSH indexing and that MeSH Now is capable of processing PubMed scale documents within a reasonable time frame. Availability: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/MeSHNow/. äMeSH Now: automatic MeSH indexing at PubMed scale via learning to rank(epmc).pdf DeepDyve Pubget Overpricing