Abstract
IT helpdesks are charged with the task of responding quickly to user queries. To give the user confidence that their query matters, the helpdesk will auto-reply to the user with confirmation that their query has been received and logged. This auto-reply may include generic ‘boiler-plate’ text that addresses common problems of the day, with relevant information and links. The approach explored here is to tailor the content of the auto-reply to the user’s problem, so as to increase the relevance of the information included. Problem classification is achieved by training a neural network on a suitable corpus of IT helpdesk email data. While this is no substitute for follow-up by helpdesk agents, the aim is that this system will provide a practical stop-gap.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alpaydin, E.: Introduction to Machine Learning. The MIT Press, Cambridge (2014)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003)
Ganegedara, T.: Natural Language Processing with TensorFlow: Teach Language to Machines Using Python’s Deep Learning Library. Packt Publishing (2018)
López, F.: Class imbalance: random sampling and data augmentation with imbalanced-learn. https://towardsdatascience.com/class-imbalance-random-sampling-and-data-augmentation- with-imbalanced-learn-63f3a92ef04a, February 2021. Accessed 09 Apr 2022
Manning, C., Raghavan, P., Schütze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008). https://nlp.stanford.edu/IR-book/
McTear, M.: The Conversational Interface. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-32967-3
Menon, S.: Stratified sampling in machine learning. https://medium.com/analytics-vidhya/stratified-sampling-in-machine-learning-f5112b5b9cfe, December 2020. Accessed 04 Apr 2022
Nayak, R.: Hands on data augmentation in NLP using NLPAUG python library. https://medium.com/codex/hands-on-data-augmentation-in-nlp-using-nlpaug-python-library-ad323c22908, April 2022. Accessed 09 Apr 2022
Powers, D.M.W.: Evaluation: from precision, recall and f-factor to ROC, informedness, markedness & correlation. Technical report SIE-07-001, Flinders University of South Australia, Adelaide, Australia (2007). https://www.researchgate.net/publication/228529307_Evaluation_From_Precision_Recall_and_F-Factor_to_ROC_Informedness_Markedness_Correlation
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Hoboken (2010)
Shahul, E.: Data augmentation in NLP: best practices from a Kaggle master. https://neptune.ai/blog/data-augmentation-nlp, November 2021. Accessed 03 Mar 2022
Wang, J., Luis, P.: The effectiveness of data augmentation in image classification using deep learning, report number: 300 (2017). http://cs231n.stanford.edu/reports/2017/pdfs/300.pdf. Accessed 04 Apr 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Nicholls, R., Fellows, R., Battle, S., Ihshaish, H. (2022). Problem Classification for Tailored Helpdesk Auto-replies. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-031-15937-4_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15936-7
Online ISBN: 978-3-031-15937-4
eBook Packages: Computer ScienceComputer Science (R0)