SMART Mail - A SMART Platform for Mail Management
Ricardo Raminhos, Eduardo Coutinho, Nuno Miranda, Maria Barbas, Paulo Branco, Teresa Gonçalves, Gil Palma
2016
Abstract
Email is a key communication format in a digital world, both for professional and/or personal usage. Exchanged messages (both human and automatically generated) have reached such a volume that processing them can be a great challenge for human users that try to do it on a daily basis and in an efficient manner. In fact, a significant amount of their time is spent searching and getting context information (normally historic information) in order to prepare a reply message or to take a decision/action, when compared to the actual time required for writing a reply. Therefore, it is of utmost importance for this process to use both automatic and semi-automatic mechanisms that allow to put email messages into context. Since context information is given, not only by historical email messages but also inferred from the relationship between contacts and/or organizations present in the messages, the existence of navigation mechanisms (and even exploration ones) between contacts and entities associated to email messages, is of fundamental importance. This is the main purpose of the SMART Mail prototype, which architecture, data visualization and exploration components and AI algorithms, are presented throughout this paper.
References
- Aberdeen, J., Burger, J., Day, D., Hirschman, L., Robinson, P., & Vilain, M. (1995). {MITRE}: description of the Alembic system used for {MUC-6}. MUC6 7895: Proceedings of the 6th conference on Message understanding (pp. 141-155). Morristown, NJ, USA: Association for Computational Linguistics.
- Baluja, S., Mittal, V., & Sukthankar, R. (2000). Applying Machine Learning For High Performance NamedEntity Extraction. In Proceedings of the Conference of the Pacific Association for Computational Linguistics, (pp. 365-378).
- Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. ICML 7806: Proceedings of the 23rd international conference on Machine learning (pp. 161-168). New York, NY, USA: ACM.
- Shawe-Taylor, J. (2000). {An Introduction to Support Vector Machines}. Cambridge University Press.
- GetResponse Homepage. (n.d.). Retrieved 2015, from GetResponse: http://www.getresponse.com/
- Gimenez, J., & Marquez, L. (2004). {SVMTool: A general POS tagger generator based on Support Vector Machines}. Proceedings of the 4th LREC.
- GTE Consultores. (2015). Retrieved from http://www.gte.pt/
- Inbox. (2015). Retrieved from https://inbox.google.com.
- Instituto Politécnico de Santarém. (2015). Retrieved from http://www.ipsantarem.pt/
- Keerthi, S., Shevade, S., Bhattacharyya, C., & Murthy, K. (2001). {Improvements to Platt's SMO Algorithm for SVM Classifier Design}. Neural Comput., 13(3), 637- 649.
- Mitchell, T. (1997). Machine Learning. McGraw-Hill.
- Nadeau, D., & Sekine, S. (2007). A survey of named entity recognition and classification. Linguisticae Investigationes, 30(1), 3-26.
- National Strategic Reference Framework (NSRF). (n.d.). Retrieved 2015, from http://www.qren.pt/np4/home.
- Quinlan, R. (1993). C4.5: Programs for Machine Learning. San Mateo, US: Morgan Kaufmann.
- Raminhos, R., Coutinhho, E., Miranda, N., Barbas, M., Branco, P., Gonçalves, T., & Palma, G. (2015). Email solutions - state-of-the-art and possible evolutions.
- Sidekick. (2015). Retrieved from http://www. getsidekick.com/
- SMART Mail webpage. (n.d.). Retrieved 2015, from http://www.viatecla.com/inovacao/smart-mail.
- Takeuchi, K., & Collier, N. (2002). Use of support vector machines in extended named entity recognition. COLING-02: proceedings of the 6th conference on Natural language learning (pp. 1-7). Morristown, NJ, USA: Association for Computational Linguistics.
- The Radicati Group, I. (n.d.). Retrieved Julho 2015, from http://www.radicati.com/wp/wpcontent/uploads/2015/07/Email-Market-2015-2019- Executive-Summary.pdf.
- Universidade de Évora. (2015). Retrieved from http://www.uevora.pt/
- Verse. (2015). (IBM) Retrieved from http://www .ibm.com/social-business/us/en/newway/
- VIATECLA. (2015). Retrieved from http://www. viatecla.com.
- Witten, I., & Frank, E. (2005). Data Mining: Practical machine learning tools and techniques (2nd ed.). San Francisco, US: Morgan Kaufmann.
- Xobni Support Homepage. (n.d.). Retrieved 2015, from Xobni Support: https://support.xobni.com/home.
- Zhang, H. (2004). The optimality of naive Bayes. Proceedings of the 17th International FLAIRS conference. AAAI Press.
Paper Citation
in Harvard Style
Raminhos R., Coutinho E., Miranda N., Barbas M., Branco P., Gonçalves T. and Palma G. (2016). SMART Mail - A SMART Platform for Mail Management . In Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-187-8, pages 378-387. DOI: 10.5220/0005814503780387
in Bibtex Style
@conference{iceis16,
author={Ricardo Raminhos and Eduardo Coutinho and Nuno Miranda and Maria Barbas and Paulo Branco and Teresa Gonçalves and Gil Palma},
title={SMART Mail - A SMART Platform for Mail Management},
booktitle={Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2016},
pages={378-387},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005814503780387},
isbn={978-989-758-187-8},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 18th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - SMART Mail - A SMART Platform for Mail Management
SN - 978-989-758-187-8
AU - Raminhos R.
AU - Coutinho E.
AU - Miranda N.
AU - Barbas M.
AU - Branco P.
AU - Gonçalves T.
AU - Palma G.
PY - 2016
SP - 378
EP - 387
DO - 10.5220/0005814503780387