Abstract: Systems and methods provide reception of a plurality of data samples for training a machine learning model and a plurality of examples associated with each of a plurality of ground truth labels for training a machine learning model, identification of all examples of the plurality of examples within each of the data samples, determination, for each identified example, of an associated one of the plurality of labels and a location of the example in the data sample, annotation of the data sample with the associated one of the plurality of labels and the location, and training of a machine learning model using the annotated data sample.

Abstract: An employee of a large organization sends a human-readable document such as an email or text message to another employee of the organization to inform the other employee of a change in availability. A trained machine-learning model extracts, from the human-readable document, data used by a leave management system (LMS) to formalize and memorialize the leave request. For example, the employee name, manager name, date leave begins, date leave ends, reason for the leave request, or any suitable combination thereof may be determined by the machine-learning model based on the human-readable document. The extracted data is provided to the LMS and the leave request is created.