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.

10.1145/2723372.2751523 http://scihub22266oqcxt.onion/10.1145/2723372.2751523 C4688018!4688018!26705375     free     free     free       Proc+ACM+SIGMOD+Int+Conf+Manag+Data 2015 ; 2015 (ä): 1729-44 Nephropedia Template TP {{tp|p=26705375|t=2015. Mining Quality Phrases from Massive Text Corpora |pdf=|usr=}}{{26705375}} gab.com Text Mining Quality Phrases from Massive Text Corpora JO: Proc ACM SIGMOD Int Conf Manag Data http://www.kidney.de/pget.php?&tw=1&pmid=26705375 #FOAvip Text data are ubiquitous and play an essential role in big data applications. However, text data are mostly unstructured. Transforming unstructured text into structured units (e.g., semantically meaningful phrases) will substantially reduce semantic ambiguity and enhance the power and efficiency at manipulating such data using database technology. Thus mining quality phrases is a critical research problem in the field of databases. In this paper, we propose a new framework that extracts quality phrases from text corpora integrated with phrasal segmentation. The framework requires only limited training but the quality of phrases so generated is close to human judgment. Moreover, the method is scalable: both computation time and required space grow linearly as corpus size increases. Our experiments on large text corpora demonstrate the quality and efficiency of the new method. Twit Text FOAVip Mining Quality Phrases from Massive Text Corpora ????Proc ACM SIGMOD Int Conf Manag Data http://www.kidney.de/pget.php?&tw=1&pmid=26705375 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26705375 #FOAvip English Wikipedia <ref>{{Cite pmid|26705375}}</ref> Mining Quality Phrases from Massive Text Corpora #MMPMID26705375 Liu J; Shang J; Wang C; Ren X; Han J Proc ACM SIGMOD Int Conf Manag Data 2015[May]; 2015 (ä): 1729-44 PMID26705375show ga Text data are ubiquitous and play an essential role in big data applications. However, text data are mostly unstructured. Transforming unstructured text into structured units (e.g., semantically meaningful phrases) will substantially reduce semantic ambiguity and enhance the power and efficiency at manipulating such data using database technology. Thus mining quality phrases is a critical research problem in the field of databases. In this paper, we propose a new framework that extracts quality phrases from text corpora integrated with phrasal segmentation. The framework requires only limited training but the quality of phrases so generated is close to human judgment. Moreover, the method is scalable: both computation time and required space grow linearly as corpus size increases. Our experiments on large text corpora demonstrate the quality and efficiency of the new method. äMining Quality Phrases from Massive Text Corpora (epmc).pdf DeepDyve Pubget Overpricing