DocumentCode :
623997
Title :
Big data, little decisions: Tightening the loop between data crunching and human expertise
Author :
Bennett, Zack ; L´Heureux, Marc G.
Author_Institution :
LexisNexis, Dayton, OH, USA
fYear :
2013
fDate :
20-24 May 2013
Firstpage :
65
Lastpage :
66
Abstract :
This presentation is a case study examining how LexisNexis uses scaled active learning on the HPCC Systems environment to focus manual topical annotations on critical documents pulled from a large corpus. The active learning system uses natural language processing and machine learning techniques to identify and present “next best” training set candidates to legal editors, combining massive parallel processing with expert human analysis to improve classifier accuracy while minimizing human effort.
Keywords :
learning (artificial intelligence); natural language processing; parallel processing; text analysis; HPCC systems environment; LexisNexis; classifier accuracy; critical documents; data crunching; expert human analysis; machine learning techniques; massive parallel processing; natural language processing; scaled active learning system; text classification; Classification algorithms; Data handling; Data storage systems; Information management; Parallel processing; Support vector machines; Training; active learning; annotations; large corpus; machine learning; text classification; training set;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Collaboration Technologies and Systems (CTS), 2013 International Conference on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4673-6403-4
Type :
conf
DOI :
10.1109/CTS.2013.6567205
Filename :
6567205
Link To Document :
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