Named Entity Recognition using Neural Networks

Total time needed: ~39 minutes
Learning Objectives
Understand how we can extract information from text such Named Entities (locations, organizations, people names, etc) using Neural Networks
Potential Use Cases
Textual Analysis, Information Extraction,
Target Audience
INTERMEDIATENLP Enthusiasts, Engineers / Developers interested in textual analysis
Go through the following annotated items in order:
ARTICLE 1. What is NER
  • What is Named Entity Recognition?
4 minutes
ARTICLE 2. Simple Example using spaCy
  • Quick example - How to use NER out of the box?
5 minutes
ARTICLE 3. Another library for NER
  • How to use Flair for NER?
10 minutes
ARTICLE 4. NER using Bi-directional Neural Network (Keras)
  • How to train your own Neural Network for NER?
15 minutes
ARTICLE 5. Another implementation example
  • How do I train my own NER(Another Example)?
15 minutes
ARTICLE 6. TimeDistributed Layer
  • What is and how to use TimeDistributed Layer?
10 minutes

Concepts Covered