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10.1016/j.patter.2020.100053 http://scihub22266oqcxt.onion/10.1016/j.patter.2020.100053 C7318947!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 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\32835308 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Patterns+(N+Y) 2020 ; 1 (4 ): 100053 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. These methods require minimal or no human intervention and can recursively learn new relational facts-instances in a fully automated and scalable manner. This paper explores the performance of tolerance rough set-based learner with respect to two important issues: scalability and its effect on concept drift, by (1) designing a new version of the semi-supervised tolerance rough set-based pattern learner (TPL 2.0), (2) adapting a tolerance form of rough set methodology to categorize linguistic patterns, and (3) extracting categorical information from a large noisy dataset of crawled web pages. This work demonstrates that the TPL 2.0 learner is promising in terms of precision@30 metric when compared with three benchmark algorithms: Tolerant Pattern Learner 1.0, Fuzzy-Rough Set Pattern Learner, and Coupled Bayesian Sets-based learner. 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 ): 100053 PMID32835308 show 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. These methods require minimal or no human intervention and can recursively learn new relational facts-instances in a fully automated and scalable manner. This paper explores the performance of tolerance rough set-based learner with respect to two important issues: scalability and its effect on concept drift, by (1) designing a new version of the semi-supervised tolerance rough set-based pattern learner (TPL 2.0), (2) adapting a tolerance form of rough set methodology to categorize linguistic patterns, and (3) extracting categorical information from a large noisy dataset of crawled web pages. This work demonstrates that the TPL 2.0 learner is promising in terms of precision@30 metric when compared with three benchmark algorithms: Tolerant Pattern Learner 1.0, Fuzzy-Rough Set Pattern Learner, and Coupled Bayesian Sets-based learner. äHarvesting Patterns from Textual Web Sources with Tolerance Rough Sets(epmc).pdf DeepDyve Pubget Overpricing