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Modeling Intention in Email

  • Couverture cartonnée
  • 116 Nombre de pages
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This book applies sophisticated machine learning techniques to a large body of email data. It presents techniques that can positiv... Lire la suite
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Description

This book applies sophisticated machine learning techniques to a large body of email data. It presents techniques that can positively impact work-related email communication and offers robust models that may be applied to future machine learning tasks.

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.

From the reviews:

The targeted audience consists mainly of computer scientists, either researchers in machine learning or professionals who use email services in their work. The book would also be useful in education, especially to generate assignments for advanced students. Readers should have a machine learning background, but in general this monograph is easily readable. It provides a brief but comprehensive introduction to the peculiarities of email problems and the appropriate methods for addressing them. The updated bibliography and related work sections are useful for further study. (Lefteris Angelis, ACM Computing Reviews, June, 2012)

Contenu

Introduction.- Email "Speech Acts".- Email Information Leaks.- Recommending Email Recipients.- User Study.- Conclusions.-Email Act Labeling Guidelines.- User Study Supporting Material.

Informations sur le produit

Titre: Modeling Intention in Email
Auteur:
Code EAN: 9783642267963
ISBN: 3642267963
Format: Couverture cartonnée
Editeur: Springer Berlin Heidelberg
Genre: Généralités et lexiques
nombre de pages: 116
Poids: 189g
Taille: H235mm x B155mm x T6mm
Année: 2013
Auflage: 2011

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