Modeling Intention in Email [recurso electrónico] : Speech Acts, Information Leaks and Recommendation Models / by Vitor R. Carvalho.

Por: Carvalho, Vitor R [author.]Colaborador(es): SpringerLink (Online service)Tipo de material: Texto

TextoSeries Studies in Computational Intelligence ; 349Editor: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011Descripción: XII, 104 p. online resourceTipo de contenido: text Tipo de medio: computer Tipo de portador: online resourceISBN: 9783642199561Tema(s): Engineering | Information systems | Artificial intelligence | Engineering | Computational Intelligence | Artificial Intelligence (incl. Robotics) | Information Systems Applications (incl.Internet)Formatos físicos adicionales: Printed edition:: Sin títuloClasificación CDD: 006.3 Clasificación LoC:Q342Recursos en línea: Libro electrónico Texto

Contenidos:

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

En: Springer eBooksResumen: 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.

Existencias ( 1 )
Notas de título ( 3 )

Existencias
Tipo de ítem	Biblioteca actual	Colección	Signatura	Copia número	Estado	Fecha de vencimiento	Código de barras
Libro Electrónico	Biblioteca Electrónica	Colección de Libros Electrónicos	Q342 (Browse shelf(Abre debajo))	1	No para préstamo		375979-2001

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

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.