There are several popular open-source libraries and frameworks for Named Entity Recognition (NER). Here are a few examples:

  1. Natural Language Toolkit (NLTK): NLTK is a popular library for NLP tasks in Python. It provides various NER functionalities, including pre-trained models for NER on different corpora.

  2. Stanford NER: Stanford NER is a widely-used NER system that is written in Java. It provides pre-trained models for recognizing named entities in text, such as person names, organizations, locations, etc.

  3. SpaCy: SpaCy is a modern and efficient library for NLP in Python. It offers pre-trained models for NER, which can be customized and fine-tuned according to specific tasks.

  4. Flair: Flair is a powerful library that combines state-of-the-art NLP models and techniques. It includes pre-trained models for NER, and it also allows you to train your own models using annotated data.

  5. CRF++: CRF++ is a well-known implementation of Conditional Random Fields (CRF), a popular approach for sequence labeling tasks like NER. It provides a command-line interface and various APIs for training and using NER models.

  6. OpenNLP: OpenNLP is a library for NLP tasks in Java. It includes a named entity recognition module that offers pre-trained models for recognizing and classifying named entities.

  7. GATE (General Architecture for Text Engineering): GATE is an open-source framework that provides a wide range of NLP tools and components, including a NER module. It allows users to design custom NER pipelines and integrate them into larger NLP applications.

  These are just a few examples, and there are many other open-source libraries and frameworks available for NER. The choice of library or framework may depend on factors such as programming language preference, ease of use, performance, and specific requirements of the NER task at hand.