Abstract: This disclosure relates generally to performance appraisal management, and more particularly to standardization of goals associated with performance appraisal. In one embodiment, a method for standardization of goals includes identifying labeled and unlabeled goals associated with a role. The goals includes template and manually created goals. Each of the template goals is associated with a class label, and includes corresponding goal description and self-comments. First and second classifiers are trained using goal description and self-comments. Candidate negative goals are identified and excluded from the goals to obtain a set of unlabeled goals. The set of unlabeled goals are classified by the first and second classifier, and a confidence score associated with the classification is determined. The unlabeled goals with high confidence score are added to labeled goals to obtain an updated set of labeled goals. The first and second classifiers are iteratively co-trained using the updated set of labeled goals.

Abstract: The present disclosure relates to methods, systems, and computer-readable media for performing root cause analysis on unstructured data to identify root causes of poor performance of an employee and to recommend one or more actions based on root causes and suggestions identified from unstructured data. Embodiments of the present disclosure may determine the presence of root causes using pattern matching, sentiment analysis and suggestion identification in the one or more employee comments and supervisor comments and further recommend actions based on suggestions from the one or more supervisor comments and the root causes of poor performance identified.

Abstract: This disclosure relates generally to performance appraisal management, and more particularly to standardization of goals associated with performance appraisal. In one embodiment, a method for standardization of goals includes identifying labeled and unlabeled goals associated with a role. The goals includes template and manually created goals. Each of the template goals is associated with a class label, and includes corresponding goal description and self-comments. First and second classifiers are trained using goal description and self-comments. Candidate negative goals are identified and excluded from the goals to obtain a set of unlabeled goals. The set of unlabeled goals are classified by the first and second classifier, and a confidence score associated with the classification is determined. The unlabeled goals with high confidence score are added to labeled goals to obtain an updated set of labeled goals. The first and second classifiers are iteratively co-trained using the updated set of labeled goals.