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10.1093/database/baw061 http://scihub22266oqcxt.onion/10.1093/database/baw061 C4852402!4852402!27141091     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Database+(Oxford) 2016 ; 2016 (ä): ä Nephropedia Template TP {{tp|p=27141091|t=2016. Chemical entity recognition in patents by combining dictionary-based and statistical approaches |pdf=|usr=}}{{27141091}} gab.com Text Chemical entity recognition in patents by combining dictionary-based and statistical approaches JO: Database (Oxford) http://www.kidney.de/pget.php?&tw=1&pmid=27141091 #FOAvip We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small.Database URL: http://biosemantics.org/chemdner-patents Twit Text FOAVip Chemical entity recognition in patents by combining dictionary-based and statistical approaches ????Database (Oxford) http://www.kidney.de/pget.php?&tw=1&pmid=27141091 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27141091 #FOAvip English Wikipedia <ref>{{Cite pmid|27141091}}</ref> Chemical entity recognition in patents by combining dictionary-based and statistical approaches #MMPMID27141091 Akhondi SA; Pons E; Afzal Z; van Haagen H; Becker BF; Hettne KM; van Mulligen EM; Kors JA Database (Oxford) 2016[]; 2016 (ä): ä PMID27141091show ga We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small.Database URL: http://biosemantics.org/chemdner-patents äChemical entity recognition in patents by combining dictionary-based a(epmc).pdf DeepDyve Pubget Overpricing