NER is an information extraction technique to identify and classify named entities in text. These entities can be pre-defined and generic like location names, organizations, time and etc, or they can be very specific like the example with the resume. NER has a wide variety of use cases in the business. I think gmail is applying NER when you are writing an email and you mention a time in your email or attaching a file, gmail offers to set a calendar notification or remind you to attach the file in case you are sending the email without an attachment. Other applications of NER include: extracting important named entities from legal, financial, and medical documents, classifying content for news providers, improving the search algorithms, and etc.

complete Jupyter notebook for implementation of state-of-the-art Named Entity Recognition with bidirectional LSTMs and ELMo.

Check out the full Articele and tutorial on how to run this project here.