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publicShareStarNamed Entity Recognition using Neural Networks
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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