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.

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. CoreNLP comes with a few pre-trained models like English models trained to structured English text for detecting names, places etc. But if the text in your domain or use case doesn't overlap domain for which the pre-trained models were built for then the pre-trained model may not work well for you.