This project is from my Master Thesis. We are going to see 4 out-of-the-box tools available completely free of charge using the Python language. These are NLP libraries that provide the possibility of doing Name Entity Recognition (NER) .

The dataset on which we trained the models is CoNLL-2003 from the Conference on Computational Natural Language Learning. The four NLP techniques used to create this tool are increasingly popular in the modern landscape:

• Flair is a powerful NLP (Natural Language Processing) library, open-sourced and developed by Zalando Research;
• SpaCy is also an open-sourced library and free for advanced NLP in Python. SpaCy is designed for production use in applications that have to process big volumes of texts.
• Then we will use the Conditional Random Fields, a class of discriminative models to prediction tasks where contextual information are used in the prediction. We will train a CRF model for named entity recognition using sklearn-crfsuite.
• To conclude, the last used tool we make available is BERT, Bidirectional Encoder Representations from Transformers, a new language representation model created by Google. BERT is created to pretrain deep bidirectional model with unlabeled text by conditioning on both left and right context in all layers.

The shared task of CoNLL-2003 concerns language-independent named entity recognition.
The structure of the training and test data is concentrated to have four types of named entities: persons, locations, organizations and names of miscellaneous entities not belonging to the previous groups.