Stanford CoreNLP: Training your own custom NER tagger.

Stanford core NLP is by far the most battle-tested NLP library out there. In a way, it is the golden standard of NLP performance today. Among various other functionalities, named entity recognization (NER) is supported in the library, what this allows is to tag important entities in a piece of text like the name of a person, place etc. Core NLP NER tagger implements CRF (conditional random field) algorithm which is one of the best ways to solve NER problem in NLP. The algorithm is trained on a tagged dataset and the output is a learned model. Basically, the model learns the information and structure in the training data and can use that to label an unseen text.