Most e-mail-filtering systems build policies from rules that use a keyword approach that groups words or phrases into standard or forbidden classifications -- such as gambling, offensive language, pornography, racism and sexual harassment -- and then apply actions -- such as deletion or quarantine -- to messages with those keywords.
That approach, however, has its limits in the English language, which is rife with ambiguous words. Blow, bust, joint and puff may be innocuous, or they may embarrass, humiliate or inflame. Using such keywords can generate false positives; avoiding them, however, can lead to false negatives -- that is, the forbidden content gets through the e-mail system.
B-Monitor applies NLP (natural language processing) techniques to e-mail filtering. Using Languistics' XML-based !metaMarker, B-Monitor automatically extracts and organizes text information to find contextual meaning. The software uses descriptive tags to classify words as parts of speech and analyze messages' explicit and implicit language content.
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2014 State of Unified Communications If you thought consumerization killed UC, think again: 70% of our 488 respondents have or plan to put systems in place. Of those, 34% will roll UC out to 76% or more of their user base. And there’s some good news for UCaaS providers.
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