Natural Language Processing (NLP) is a field of study that deals with the interaction between natural (human) languages and computers. Python, being a powerful programming language, has gained immense popularity in the field of NLP. Python libraries for NLP have been developed to make it easier for developers to create complex NLP applications without having to start from scratch.
1. Natural Language Toolkit (NLTK)
NLTK is one of the most popular NLP libraries in Python. It provides tools to work with human language data, specifically text. The library contains over 50 corpora and lexical resources including WordNet, which is a large lexical database for English. NLTK also comes with a range of natural language processing tasks such as tokenization, stemming, tagging, parsing, and semantic reasoning.
2. TextBlob
TextBlob is a simple and easy-to-use NLP library in Python. It provides a consistent API for common NLP tasks such as noun phrase extraction, sentiment analysis, classification, translation, and more. TextBlob also has a built-in sentiment analyzer that returns the polarity and subjectivity of a given text.
3. spaCy
spaCy is a fast and efficient NLP library in Python. It is designed for production use and is capable of handling large volumes of text. spaCy provides an easy-to-use API for common NLP tasks such as named entity recognition, part-of-speech tagging, dependency parsing, sentiment analysis, and more. It also has pre-trained models for a range of languages and domains.
4. Pattern
Pattern is an NLP library in Python that focuses on web mining, natural language generation, and machine learning. Pattern provides functionality for part-of-speech tagging, sentiment analysis, entity recognition, and more. It also has a built-in web crawler that can be used to extract data from the web.
5. Gensim
Gensim is an NLP library in Python that specializes in topic modeling and similarity retrieval. It provides an API for training and using topic models such as Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). Gensim also has functionality for similarity retrieval using algorithms such as cosine similarity and Jaccard similarity.
6. Stanford CoreNLP
Stanford CoreNLP is a suite of NLP tools in Python that provides a range of tasks such as tokenization, part-of-speech tagging, dependency parsing, and named entity recognition. It also provides functionality for sentiment analysis and coreference resolution. Stanford CoreNLP has pre-trained models for English, Arabic, Chinese, French, German, and Spanish.
7. PyStanfordDependencies
PyStanfordDependencies is an NLP library in Python that provides a lightweight interface for parsing Stanford Dependencies output. It provides an easy-to-use API for extracting dependency parses from text using Stanford CoreNLP or Stanford Parser.
8. Polyglot
Polyglot is an Natural Language Processing library in Python that provides support for multiple languages. It provides functionality for part-of-speech tagging, named entity recognition, sentiment analysis, and more. Polyglot also has pre-trained models for over 130 languages.
9. NLTK-Contrib
NLTK-Contrib is a collection of NLP tools and resources built on top of NLTK. It provides additional functionality for common NLP tasks such as sentiment analysis, named entity recognition, and dependency parsing. NLTK-Contrib also provides tools for working with specific corpora such as Brown Corpus and Treebank.
10. PyNLPl
PyNLPl is an Natural Language Processing library in Python that provides functionality for working with natural language data. It provides functionality for part-of-speech tagging, named entity recognition, chunking, and more. PyNLPl also has support for a range of formats such as CoNLL, OpenDocument, and TIGER Corpus.
Conclusion
Python has a range of powerful libraries for Natural Language Processing. The libraries listed in this post provide a wide range of functionality for common NLP tasks such as tokenization, part-of-speech tagging, named entity recognition, sentiment analysis, and more. Choosing the right library for your project depends on your specific needs and requirements. We hope this post has helped you in finding the right Python library for your NLP project.
Want to learn more about Natural Language Processing with Python, checkout the Python Official Documentation for detail.