Author: Vitor R. Carvalho
Publisher: Springer Science & Business Media
Published: 2011-11-06
Total Pages: 111
ISBN-13: 3642199550
DOWNLOAD EBOOKEveryday more than half of American adult internet users read or write email messages at least once. The prevalence of email has significantly impacted the working world, functioning as a great asset on many levels, yet at times, a costly liability. In an effort to improve various aspects of work-related communication, this work applies sophisticated machine learning techniques to a large body of email data. Several effective models are proposed that can aid with the prioritization of incoming messages, help with coordination of shared tasks, improve tracking of deadlines, and prevent disastrous information leaks. Carvalho presents many data-driven techniques that can positively impact work-related email communication and offers robust models that may be successfully applied to future machine learning tasks.
Book Synopsis Modeling Intention in Email by : Vitor R. Carvalho
Download or read book Modeling Intention in Email written by Vitor R. Carvalho and published by Springer Science & Business Media. This book was released on 2011-11-06 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Everyday more than half of American adult internet users read or write email messages at least once. The prevalence of email has significantly impacted the working world, functioning as a great asset on many levels, yet at times, a costly liability. In an effort to improve various aspects of work-related communication, this work applies sophisticated machine learning techniques to a large body of email data. Several effective models are proposed that can aid with the prioritization of incoming messages, help with coordination of shared tasks, improve tracking of deadlines, and prevent disastrous information leaks. Carvalho presents many data-driven techniques that can positively impact work-related email communication and offers robust models that may be successfully applied to future machine learning tasks.