Modeling Intention in Email : speech acts, information leaks and recommendation models
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-re...
保存先:
| 第一著者: | |
|---|---|
| フォーマット: | Livre numérique |
| 言語: | Anglais |
| 出版事項: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| 版: | 1st ed. 2011. |
| シリーズ: | Studies in Computational Intelligence
349 |
| オンライン･アクセス: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| 注記: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Modeling intention in email, speech acts, information leaks and recommendation models, Vitor R. Carvalho, Berlin, Springer, 2011, 1 vol. (XI-104 p.), Studies in computational intelligence, 978-3-642-19955-4 |
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| 100 | 1 | |a Carvalho, Vitor R. | |
| 245 | 1 | 0 | |a Modeling Intention in Email : |b speech acts, information leaks and recommendation models |c by Vitor R. Carvalho. |
| 250 | |a 1st ed. 2011. | ||
| 260 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg. | ||
| 260 | |a Cham : |b Springer Nature, |c [20..]. | ||
| 490 | 0 | |a Studies in Computational Intelligence |v 349 |x 1860-9503 | |
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| 505 | 1 | |a Introduction Email Speech Acts Email Information Leaks Recommending Email Recipients.- User Study Conclusions.-Email Act Labeling Guidelines User Study Supporting Material | |
| 506 | |a Accès en ligne pour les établissements français bénéficiaires des licences nationales | ||
| 506 | |a Accès soumis à abonnement pour tout autre établissement | ||
| 506 | |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 | ||
| 520 | |a 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 | ||
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