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

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dc.contributor.authorCouto, Julia
dc.contributor.authorTomaz, Laura
dc.contributor.authorGodoy, Julia
dc.contributor.authorKniest, Davi
dc.contributor.authorCallegari, Daniel
dc.contributor.authorMeneguzzi, Felipe
dc.contributor.authorRuiz, Duncan
dc.date.accessioned2021-12-24T17:23:10Z
dc.date.available2021-12-24T17:23:10Z
dc.date.issued2022-01-04
dc.description.abstractTroubleshooting 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.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2022.097
dc.identifier.isbn978-0-9981331-5-7
dc.identifier.urihttp://hdl.handle.net/10125/79427
dc.language.isoeng
dc.relation.ispartofProceedings of the 55th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectText Analytics
dc.subjectdeep learning
dc.subjectmachine learning
dc.subjecttext categorization
dc.subjecttext similarity
dc.subjecttroubleshooting automation
dc.titleOn the use of Machine Learning and Deep Learning for Text Similarity and Categorization and its Application to Troubleshooting Automation
dc.type.dcmitext

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