Alecsa: Attentive Learning for Email Categorization using Structural Aspects

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• 作者：Mostafa Dehghani^a ; ^b ; ^{dehghani@uva.nl" class="auth_mail" title="E-mail the corresponding author} ; Azadeh Shakery ; ^a ; ^c ; ^{shakery@ut.ac.ir" class="auth_mail" title="E-mail the corresponding author} ; Maryam S. Mirian^d ; ^{mmirian@ut.ac.ir" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Email categorization ; Email structural aspects ; Active decision fusion learning ; Reinforcement learning
• 刊名：Knowledge-Based Systems
• 出版年：2016
• 出版时间：15 April 2016
• 年：2016
• 卷：98
• 期：Complete
• 页码：44-54
• 全文大小：1116 K

文摘

Due to the enormous volume of email data generated each day, email management has become a vital area of research. Among the email management tasks, automatic email categorization is one of the most interesting problems. However, the dynamic nature of email data makes the email categorization problem difficult to address for traditional machine learning approaches. In this paper, we propose Alecsa as an attentive learning approach for automatic email categorization. Alecsa aims to simulate the dynamic behavior of users while they attempt to categorize a new email. For this purpose, email categorization problem in Alecsa is cast to a decision-making problem, and an attention control framework is employed to dynamically choose a sequence of structural aspects of the email as the distinguishing factors for categorization. We have analytically evaluated the proposed approach on the Enron–Bekkerman datasets. The evaluation results indicate the unprecedented power of Alecsa toward modeling the dynamic essence of the email categorization problem in terms of effectiveness as well as efficiency.