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10.1016/j.patter.2020.100053 http://scihub22266oqcxt.onion/10.1016/j.patter.2020.100053 C7318947/?report=reader!7318947!32835308     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Patterns+(N+Y) 2020 ; 1 (4): ä Nephropedia Template TP {{tp|p=32835308|t=2020. Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets |pdf=|usr=}}{{32835308}} gab.com Text Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets JO: Patterns (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=32835308 #FOAvip Construction of knowledge repositories from web corpora by harvesting linguistic patterns is of benefit for many natural language processing applications that rely on question-answering schemes. Key challenges encountered when harvesting linguistic patterns because of the iterative nature of never-ending learning are the following: number of training examples are few, relational facts might belong to more than one category depending on their context, and the emergence of concept drift where new relational facts end up being miscategorized. We propose a tolerance rough set-based learner to tackle issues of scalability and its effect on concept drift. Twit Text FOAVip Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets ????Patterns (N Y) http://www.kidney.de/pget.php?&tw=1&pmid=32835308 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32835308 #FOAvip English Wikipedia <ref>{{Cite pmid|32835308}}</ref> Harvesting Patterns from Textual Web Sources with Tolerance Rough Sets #MMPMID32835308 Moghaddam HR; Ramanna S Patterns (N Y) 2020[Jul]; 1 (4): ä PMID32835308show ga Construction of knowledge repositories from web corpora by harvesting linguistic patterns is of benefit for many natural language processing applications that rely on question-answering schemes. Key challenges encountered when harvesting linguistic patterns because of the iterative nature of never-ending learning are the following: number of training examples are few, relational facts might belong to more than one category depending on their context, and the emergence of concept drift where new relational facts end up being miscategorized. We propose a tolerance rough set-based learner to tackle issues of scalability and its effect on concept drift. äHarvesting Patterns from Textual Web Sources with Tolerance Rough Sets(epmc).pdf DeepDyve Pubget Overpricing