Named Entity Recognition (NER) is a technique in natural language processing (NLP) that focuses on identifying and classifying entities. The purpose of NER is to automatically extract structured information from unstructured text, enabling machines to understand and categorize entities in a meaningful manner for various applications like text summarization, building knowledge graphs, question answering, and knowledge graph construction.

There are various methods to implement NER:

1. Lexicon Based Method
2. Rule Based Method
3. Machine Learning-Based Method 3.1 Bi-LSTM with CRF 3.2 Using fine tuned BERT

In this project I have implemented all these methods and calculated F1-score of all those models.