Parsing semi-structured records with free-form text log messages into structured templates is the first and crucial step that enables further analysis. NuLog presents a novel parsing technique that utilizes a self-supervised learning model and formulates the parsing task as masked language modeling (MLM). In the process of parsing, the model extracts summarizations from the logs in the form of a vector embedding. This allows the coupling of the MLM as pre-training with a downstream anomaly detection task.
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