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A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1093/nar/gky428 http://scihub22266oqcxt.onion/10.1093/nar/gky428 C6030907!6030907!29788413     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       Nucleic+Acids+Res 2018 ; 46 (Web Server issue): W523-9 Nephropedia Template TP {{tp|p=29788413|t=2018. ezTag: tagging biomedical concepts via interactive learning |pdf=|usr=}}{{29788413}} gab.com Text ezTag: tagging biomedical concepts via interactive learning JO: Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=29788413 #FOAvip Recently, advanced text-mining techniques have been shown to speed up manual data curation by providing human annotators with automated pre-annotations generated by rules or machine learning models. Due to the limited training data available, however, current annotation systems primarily focus only on common concept types such as genes or diseases. To support annotating a wide variety of biological concepts with or without pre-existing training data, we developed ezTag, a web-based annotation tool that allows curators to perform annotation and provide training data with humans in the loop. ezTag supports both abstracts in PubMed and full-text articles in PubMed Central. It also provides lexicon-based concept tagging as well as the state-of-the-art pre-trained taggers such as TaggerOne, GNormPlus and tmVar. ezTag is freely available at http://eztag.bioqrator.org. Twit Text FOAVip ezTag: tagging biomedical concepts via interactive learning ????Nucleic Acids Res http://www.kidney.de/pget.php?&tw=1&pmid=29788413 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29788413 #FOAvip English Wikipedia <ref>{{Cite pmid|29788413}}</ref> ezTag: tagging biomedical concepts via interactive learning #MMPMID29788413 Kwon D; Kim S; Wei CH; Leaman R; Lu Z Nucleic Acids Res 2018[Jul]; 46 (Web Server issue): W523-9 PMID29788413show ga Recently, advanced text-mining techniques have been shown to speed up manual data curation by providing human annotators with automated pre-annotations generated by rules or machine learning models. Due to the limited training data available, however, current annotation systems primarily focus only on common concept types such as genes or diseases. To support annotating a wide variety of biological concepts with or without pre-existing training data, we developed ezTag, a web-based annotation tool that allows curators to perform annotation and provide training data with humans in the loop. ezTag supports both abstracts in PubMed and full-text articles in PubMed Central. It also provides lexicon-based concept tagging as well as the state-of-the-art pre-trained taggers such as TaggerOne, GNormPlus and tmVar. ezTag is freely available at http://eztag.bioqrator.org. äezTag: tagging biomedical concepts via interactive learning (epmc).pdf DeepDyve Pubget Overpricing