Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

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.

10.1155/2016/4248026 http://scihub22266oqcxt.onion/10.1155/2016/4248026 C4749772!4749772!26942193     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Res+Int 2016 ; 2016 (ä): ä Nephropedia Template TP {{tp|p=26942193|t=2016. ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition |pdf=|usr=}}{{26942193}} gab.com Text ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition JO: Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26942193 #FOAvip Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities. Twit Text FOAVip ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition ????Biomed Res Int http://www.kidney.de/pget.php?&tw=1&pmid=26942193 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26942193 #FOAvip English Wikipedia <ref>{{Cite pmid|26942193}}</ref> ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition #MMPMID26942193 Akkasi A; Varo?lu E; Dimililer N Biomed Res Int 2016[]; 2016 (ä): ä PMID26942193show ga Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities. äChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognit(epmc).pdf DeepDyve Pubget Overpricing