Abstract
This paper describes a tool called ACE, which stands for Assistente Cognitivo de E-mail (Cognitive Email Assistant). It is an application that reads customers emails from a general entrance email box sent to Banco do Brasil. Afterwards, it classifies the emails by their content (message body) and forwards them to other four Specific Email Boxes (SEBs), according to the demand or business of the customer found in the email body. The application was created to automate the screening process of an email box that receives up to 4,000 emails per day. Before ACE existed, the screening process was manually done by up to eight business assistants (employees) of the company. When the application started being used, the number of employees working on the General Email Box (GEB) was reduced to one or two. They are still necessary because ACE does not classify all emails received in the GEB. The machine learning algorithm used in this task is a Support Vector Machine (SVM) with a linear kernel. The efficiency of the system is assured by a curation process coupled with a self-feeding strategy. The F1-Score of the system is 0.9048.
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References
Cohen, W.W., et al.: Learning rules that classify e-mail. In: AAAI Spring Symposium on Machine Learning in Information Access, pp. 18–25 (1996)
Klimt, B., Yang, Y.: The Enron corpus: a new dataset for email classification research. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 217–226. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30115-8_22
Zhang, F., Xu, K.: Annotation and classification of an email importance corpus. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 651–656 (2015)
Nenkova, A., Bagga, A.: Email classification for contact centers. In: Proceedings of the 2003 ACM Symposium on Applied Computing, pp. 789–792 (2003)
Alkhereyf, S., Rambow, O.: Work hard, play hard: email classification on the Avocado and Enron corpora. In: Proceedings of TextGraphs-11: The Workshop on Graph-Based Methods for Natural Language Processing, pp. 57–65 (2017)
Corston-Oliver, S., Ringger, E., Gamon, M., Campbell, R.: Task-focused summarization of email. In: Text Summarization Branches Out, pp. 43–50 (2004)
Zhang, R., Tetreault, J.: This email could save your life: introducing the task of email subject line generation. arXiv preprint arXiv:1906.03497 (2019)
Saini, N., Saha, S., Bhattacharyya, P.: Cascaded SOM: an improved technique for automatic email classification. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2018)
Sharaff, A., Gupta, H.: Extra-tree classifier with metaheuristics approach for email classification. In: Bhatia, S.K., Tiwari, S., Mishra, K.K., Trivedi, M.C. (eds.) Advances in Computer Communication and Computational Sciences. AISC, vol. 924, pp. 189–197. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-6861-5_17
Youn, S.: SPONGY (SPam ONtoloGY): email classification using two-level dynamic ontology. Sci. World J. 2014 (2014). 11 pages
Gomes, S.R., et al.: A comparative approach to email classification using Naive Bayes classifier and hidden Markov model. In: 2017 4th International Conference on Advances in Electrical Engineering (ICAEE), pp. 482–487 (2017)
Grbovic, M., Halawi, G., Karnin, Z., Maarek, Y.: How many folders do you really need?: classifying email into a handful of categories. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 869–878 (2014)
Harisinghaney, A., Dixit, A., Gupta, S., Arora, A.: Text and image based spam email classification using KNN, Naive Bayes and Reverse DBSCAN algorithm. In: 2014 International Conference on Reliability Optimization and Information Technology (ICROIT), pp. 153–155 (2014)
Mujtaba, G., Shuib, L., Raj, R.G., Majeed, N., Al-Garadi, M.A.: Email classification research trends: review and open issues. IEEE Access 5, 9044–9064 (2017)
Li, M., Park, Y., Ma, R., Huang, H.Y.: Business email classification using incremental subspace learning. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 625–628 (2012)
Sethi, H., Sirohi, A., Thakur, M.K.: Intelligent mail box. In: Satapathy, S.C., Mandal, J.K., Udgata, S.K., Bhateja, V. (eds.) Information Systems Design and Intelligent Applications. AISC, vol. 435, pp. 441–450. Springer, New Delhi (2016). https://doi.org/10.1007/978-81-322-2757-1_44
Pereira, H., Teixeira, P., Oliveira, L.C.: Email2Vmail — an email reader. In: Mamede, N.J., Trancoso, I., Baptista, J., das Graças Volpe Nunes, M. (eds.) PROPOR 2003. LNCS (LNAI), vol. 2721, pp. 189–192. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-45011-4_29
Nunes, A.V., Freitas, C.O., Paraiso, E.C.: Detecção de Assédio Moral em e-mails in I Student Workshop on Information and Human Language Technology, São Carlos. I Student Workshop on Information and Human Language Technology-7th Brazilian Symposium in Information and Human Language Technology. POA: SBC 1, pp. 01–05 (2009)
Song, Y., Lee, C.-J.: Learning user embeddings from emails. In: Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, vol. 2, pp. 733–738 (2017)
Bhadra, A., Hitawala, S., Modi, R., Salunkhe, S.: Email classification using supervised learning algorithms. In: Saeed, K., Chaki, N., Pati, B., Bakshi, S., Mohapatra, D.P. (eds.) Progress in Advanced Computing and Intelligent Engineering. AISC, vol. 564, pp. 81–90. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-6875-1_9
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Faria de Azevedo, R. et al. (2020). Screening of Email Box in Portuguese with SVM at Banco do Brasil. In: Quaresma, P., Vieira, R., Aluísio, S., Moniz, H., Batista, F., Gonçalves, T. (eds) Computational Processing of the Portuguese Language. PROPOR 2020. Lecture Notes in Computer Science(), vol 12037. Springer, Cham. https://doi.org/10.1007/978-3-030-41505-1_15
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