Training a Named Entity Recognition Model Without Data

Named Entity Recognition(NER) is the task of recognizing entity names, such as person name, locations, and organizations, within a text. This task serves as a fundamental module for various NLP applications including chatbots, search engines, and translation systems. We can find NER datasets for generic entities easily, but obtaining data for specific domains can be challenging. Labeling NER data is more difficult than simple text classification, making it challenging to create large-scale domain-specific NER datasets. In this post, I will demonstrate how to train NER model without any labeled data.