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- W3199938765 abstract "The email overload problem has been discussed in numerous email-related studies. One of the possible solutions to this problem is email prioritization, which is the act of automatically predicting the importance levels of received emails and sorting the user’s inbox accordingly. Several learning-based methods have been proposed to address the email prioritization problem using content features as well as social features. Although these methods have laid the foundation works in this field of study, the reported performance is far from being practical. Recent works on deep neural networks have achieved good results in various tasks. In this paper, the authors propose a novel email prioritization model which incorporates several deep learning techniques and uses a combination of both content features and social features from email data. This method targets Vietnamese emails and is tested against a self-built Vietnamese email corpus. Conducted experiments explored the effects of different model configurations and compared the effectiveness of the new method to that of a previous work." @default.
- W3199938765 created "2021-09-27" @default.
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- W3199938765 date "2021-09-12" @default.
- W3199938765 modified "2023-10-14" @default.
- W3199938765 title "Combining content and social features in a deep learning approach to Vietnamese email prioritization" @default.
- W3199938765 doi "https://doi.org/10.21553/rev-jec.271" @default.
- W3199938765 hasPublicationYear "2021" @default.
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