Semi-supervised deep learning based named entity recognition model to parse education section of resumes
Neural Computing and Applications
A job seeker’s resume contains several sections, including educational qualifications. Educational qualifications capture the knowledge and skills relevant to the job. Machine processing of the education sections of resumes has been a difficult task. In this paper, we attempt to identify educational institutions’ names and degrees from a resume’s education section. Usually, a significant amount of annotated data is required for neural network-based named entity recognition techniques. A semi-supervised approach is used to overcome the lack of large annotated data. We trained a deep neural network model on an initial (seed) set of resume education sections. This model is used to predict entities of unlabeled education sections and is rectified using a correction module. The education sections containing the rectified entities are augmented to the seed set. The updated seed set is used for retraining, leading to better accuracy than the previously trained model. This way, it can provide a high overall accuracy without the need of large annotated data. Our model has achieved an accuracy of 92.06% on the named entity recognition task.
Gaur, Bodhvi; Saluja, Gurpreet Singh; Sivakumar, Hamsa Bharathi; and Singh, Sanjay, "Semi-supervised deep learning based named entity recognition model to parse education section of resumes" (2020). Open Access Archive. 466.