Making personnel selection smarter through word embeddings: A graph-based approach

This paper employs techniques and algorithms from the fields of natural language processing, graph representation learning and word embeddings to assist project managers in the task of personnel selection. To do so, our approach initially represents multiple textual documents as a single graph. Then, it computes word embeddings through representation learning on graphs and performs feature selection. Finally, it builds a classification model that is able to estimate how qualified a candidate employee is to work on a given task, taking as input only the descriptions of the tasks and a list of word embeddings. Our approach differs from the existing ones in that it does not require the calculation of key performance indicators or any other form of structured data in order to operate properly. For our experiments, we retrieved data from the Jira issue tracking system of the Apache Software Foundation. The evaluation results show, in most cases, an increase of 0.43% in the accuracy of the proposed classification models when compared against a widely-adopted baseline method, while their validation loss is significantly decreased by 65.54%.