SYSTEM FOR STANDARDIZATION OF GOAL SETTING IN PERFORMANCE APPRAISAL PROCESS

摘要

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.

主权项

1. A processor-implemented method for standardization of goals associated with a performance appraisal, the method comprising steps of:
(a) identifying, via one or more hardware processors, a plurality of labeled goals and a plurality of unlabeled goals from a plurality of goals associated with a role, the plurality of goals comprising a plurality of template goals and a plurality of manually created goals, each of the plurality of template goals associated with a class label, and wherein each of the plurality of goals associated with a corresponding goal description and corresponding self-comments; (b) training a first classifier using the goal description of the plurality of labelled goals associated with the role, via the one or more hardware processors; (c) training a second classifier using the self-comments of the plurality of labelled goals associated with the role, via the one or more hardware processors; (d) identifying candidate negative goals from the plurality of goals, via the one or more hardware processors; (e) excluding the candidate negative goals from the plurality of unlabeled goals to obtain a set of unlabeled goals, via the one or more hardware processors; (f) classifying the set of unlabeled goals into a plurality of classes by the first classifier and the second classifier, via the one or more hardware processors; (g) determining a confidence score associated with the classification of each of the set of unlabeled goals into the plurality of classes, via the one or more hardware processors; (h) adding from amongst the set of unlabeled goals, one or more unlabeled goals to the plurality of labeled goals to obtain an updated set of labeled goals, the one or more unlabeled goals associated with a class label for each of the first classifier and the second classifier, the one or more unlabeled goals having the confidence score greater than or equal to a threshold value, via the one or more hardware processors; and (i) iteratively co-training the first classifier and the second classifier using the updated set of labeled goals for a threshold number of iterations by performing steps (b)-(h), via the one or more hardware processors.