Introduction
One of the most useful applications of NLP technology is information extraction from unstructured texts — contracts, financial documents, healthcare records, etc. — that enables automatic data query to derive new insights. Traditionally, named entity recognition has been widely used to identify entities inside a text and store the data for advanced querying and filtering. However, if we want to semantically understand the unstructured text, NER alone is not enough since we don't know how the entities are related to each other. Performing joint NER and relation extraction will open up a whole new way of information retrieval through knowledge graphs where you can navigate across different nodes to discover hidden relationships. Therefore, performing these tasks jointly will be beneficial.