There are several popular frameworks and libraries used in natural language processing (NLP). Some of them are:

  1. Natural Language Toolkit (NLTK): NLTK is a widely used NLP library in Python. It provides easy-to-use interfaces for tasks like tokenization, stemming, tagging, parsing, and semantic reasoning.

  2. spaCy: spaCy is another powerful NLP library in Python. It is known for its fast and efficient processing capabilities and supports various tasks like tokenization, POS tagging, named entity recognition, and dependency parsing.

  3. Stanford CoreNLP: Stanford CoreNLP is a Java-based NLP library that provides a wide range of tools for processing and analyzing natural language text. It offers features like tokenization, POS tagging, parsing, sentiment analysis, named entity recognition, and coreference resolution.

  4. Gensim: Gensim is a robust Python library for topic modeling and document similarity analysis. It provides an implementation of several popular algorithms such as Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), and Word2Vec.

  5. TensorFlow: Although TensorFlow is primarily known as a deep learning framework, it also offers useful tools for NLP tasks. It provides APIs for building and training neural networks for tasks like language modeling, text classification, and machine translation.

  6. PyTorch: PyTorch is another deep learning framework that is widely used in NLP research and development. It provides dynamic computational graphs and enables easy implementation of complex models for tasks like sentiment analysis, machine translation, and named entity recognition.

  7. Apache OpenNLP: Apache OpenNLP is a Java-based library that offers a wide range of NLP tools such as tokenization, sentence segmentation, named entity recognition, part-of-speech tagging, chunking, and parsing.

  8. AllenNLP: AllenNLP is a powerful NLP library built on top of PyTorch. It provides pre-trained models and tools for tasks like reading comprehension, semantic role labeling, coreference resolution, and text classification.

  These are just a few examples of popular frameworks and libraries used in NLP. The choice of framework/library often depends on the specific task, programming language preference, and the level of customization and flexibility required.