Fine-Tuning a Named Entity Recognition Model using Data Augmentation and Oracle-based learning

Named Entity Recognition (NER) is one of the most popular natural language processing tools used for detecting entities in text in the production environment or as an intermediate process in more complicated NLP tasks. Popular NER models struggle to pick up names from certain demographics due to the lack of training with domain specific data. The following paper presents a novel approach to fine tuning a Named Entity Recognition model to detect names from different areas or ethnic groups. The proposed solution includes using an oracle to annotate data for fine tuning the model, while also replacing generic entities with a custom set of entities from the proposed demographic, in this case India, in an attempt to improve domain specific performance, like for an organization that operates in India and primarily serves Indian clients.