Predictive Coding: Networks and Trees

TwitterLinkedInFacebookGoogle GmailYahoo MailAOL MailEmailPocketEvernoteInstapaperShare

Inside the BoxIn describing predictive coding systems, it’s important to distinguish document-based systems from corpus-based systems. Document-based systems make their predictions based on the similarity of each document to a single, previously-categorized document. Corpus-based systems are, in addition, able to use higher-order properties of groups of previously-categorized documents to make their predictions. Because of this advantage, corpus-based systems are less affected by errors in coding individual documents. Continue reading

TwitterLinkedInFacebookGoogle GmailYahoo MailAOL MailEmailPocketEvernoteInstapaperShare

Ediscovery: Information is Free

TwitterLinkedInFacebookGoogle GmailYahoo MailAOL MailEmailPocketEvernoteInstapaperShare

Infinity_hexagonal_axonometric_svg_sml, © Nevit Dilmen [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0) or GFDL (http://www.gnu.org/copyleft/fdl.html)], via Wikimedia CommonsOn August 3, 2015, ediscovery SAAS provider Logikcull unveiled the first all-inclusive, flat rate pricing plans in the ediscovery industry. I interviewed Logikcull’s CEO, Andy Wilson, about his company and its business model. What follows is an abridged version of that interview, vetted by Andy for accuracy. Continue reading

TwitterLinkedInFacebookGoogle GmailYahoo MailAOL MailEmailPocketEvernoteInstapaperShare