spaCy

SpaCy is a free, open-source library for Natural Language Processing (NLP) written in Python and Cython. It was created in 2015 by Matthew Honnibal and Ines Montani, and has since become one of the most popular NLP libraries used by developers and data scientists worldwide.

SpaCy is both fast and easy to use, performing faster than other leading NLP libraries, such as NLTK and CoreNLP. It is also highly accurate, and is capable of performing a wide variety of tasks such as processing sentences into parts of speech (POS) tags, phrase chunking, named-entity recognition, part-of-speech tagging, dependency parsing, coreference resolution, text classification, and sentiment analysis.

Furthermore, SpaCy features some unique and advanced features for building custom models. It offers various training algorithms for structured models, such as text classification, named entity recognition and part-of-speech tagging, as well as unstructured models such as dependency parsing and sentiment analysis. It also supports a range of language models, such as word2vec, GloVe, and character-level embedding.

In 2016, SpaCy was built into a larger suite of open source libraries for Python (the ‘Natural Language Toolkit’). These libraries allow developers to extract more complex language information, such as detecting similarities between words and phrases, or recognizing that a sentence is related to a specific topic.

As of 2021, SpaCy has grown to become one of the most popular and comprehensive libraries for text processing and understanding. It continues to be the library of choice for NLP tasks due to its efficient algorithms, robust features, and ease of use.