On the use of Machine Learning and Deep Learning for Text Similarity and Categorization and its Application to Troubleshooting Automation

Abstract

Troubleshooting is a labor-intensive task that includes repetitive solutions to similar problems. This task can be partially or fully automated using text-similarity matching to find previous solutions, lowering the workload of technicians. We develop a systematic literature review to identify the best approaches to solve the problem of troubleshooting automation and classify incidents effectively. We identify promising approaches and point in the direction of a comprehensive set of solutions that could be employed in solving the troubleshooting automation problem. 1 Available at https://archive.ics.uci.edu/ml/ datasets/reuters-21578+text+categorization+ collection. Accessed in May 2021. 2 Available at https://archive.ics.uci.edu/ml/ datasets/Twenty+Newsgroups. Accessed in May 2021.