The 8 Top Python Libraries for Natural Language Processing (NLP) in 2021

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This article was published as part of the Data Science Blogathon.


Natural language processing (PNL) is a field located at the convergence of data science and Artificial Intelligence (HE) that, when it comes down to the basics, it's about teaching machines how to understand human dialects and extract meaning from text. This is, what's more, the reason why artificial intelligence is essential for NLP projects.

Then, What is the reason many companies care about NLP? Basically, in light of the fact that these advances can give them an expansive scope, important insights and fixes that address language-related issues buyers may encounter when cooperating on an item.

Then, in this article, we will cover the 8 main libraries and natural language processing tools (NLP) that could be useful for building real-world projects. So let's get started!


Table of Contents

  1. Natural Language Toolkit (NLTK)
  2. GenSim
  3. SPACE
  4. CoreNLP
  5. TextBlob
  6. AllenNLP
  7. polygloto
  8. scikit-learn

Natural Language Toolkit (NLTK)

NLTK is the main library for creating Python projects to work with human language data. Provides easy-to-use interfaces for more than 50 corpus and lexical assets such as WordNet, along with a configuration of text preprocessing libraries for labeling, analysis, classification, derivation, Tokenization and Semantic Reasoning Wrappers for NLP Libraries and an Active Conversational Discussion. NLTK is accessible for Windows, Mac OS and Linux. The best part is that NLTK is a free company, open source and driven by local areas. It also has some downsides. It is slow and difficult to meet the demands of production use. The learning curve is somewhat steep. Some of the features provided by NLTK are;

  • Entity extraction
  • Labeling part of the voice
  • Tokenización
  • Analyzing
  • Semantic reasoning
  • Derivative
  • Text classification

For more information, consult the official documentation: Link


Gensim is a famous Python library for natural language processing tasks. Provides a special feature to identify semantic similarities between two documents by using vector space modeling and theme modeling toolkit. All algorithms in GenSim are independent of memory with respect to the size of the corpus, which means we can process inputs larger than RAM. Provides a set of algorithms that are very useful in natural language tasks such as the hierarchical Dirichlet process (HDP), random projections (RP), the latent dirichlet assignment (LDA), latent semantic analysis (LSA / SVD / LSI) or deep learning by word2vec. . GenSim's most advanced feature is its processing speed and fantastic memory usage optimization.. GenSim's main uses include data analysis, text generation applications (chatbots) and semantic search applications. GenSim relies heavily on SciPy and NumPy for scientific computing.


For more information, consult the official documentation: Link.


SpaCy is an open source Python natural language processing library. It is designed primarily for production use, to build real world projects and helps to handle a large amount of text data. This toolkit is written in Python in Cython, making it much faster and more efficient to handle a large amount of text data. Some of the features of SpaCy are shown below:

  • Provide multi-formation transformers like BERT
  • It is much faster than other libraries.
  • Provides linguistically motivated tokenization in more than 49 Languages
  • Provides functionalities such as text classification, sentence segmentation, lematización, tagging part of speech, named entity recognition and many more.
  • That
    has 55 pipelines trained in more than 17 Languages.

For more information, consult the official documentation: Link.


Stanford CoreNLP contains a grouping of human language innovation instruments. It means making the use of semantic analysis tools for a piece of text simple and competent. With CoreNLP, can extract a wide range of text properties (as part of voice tagging, named entity recognition, etc.) in a couple of lines of code.

Since CoreNLP is written in Java, prompts for Java to be entered on your device. Nevertheless, offers programming interfaces for some popular programming languages, including Python. The tool consolidates various Stanford NLP tools, like sentiment analysis, the part of speech tagger (POS), learning boot patterns, the analyzer, the named entity recognizer (DOWN), the co-reference resolution system, to give some examples. What's more, CoreNLP maintains four separate dialects of English: Arab, chino, German, French and Spanish.


For more information, consult the official documentation: Link.


TextBlob is an open source natural language processing library in python (Python 2 and Python 3) with NLTK technology. It is the fastest NLP tool among all libraries. It is beginner friendly. It is a must-have learning tool for data scientist enthusiasts who are beginning their journey with Python and NLP.. Provides an easy interface to help beginners and has all the basic NLP functionalities, as sentiment analysis, phrase extraction, analysis and many more. Some of the features of TextBlob are shown below:

  • Sentiment analysis
  • Analyzing
  • Frequencies of words and phrases
  • Labeling part of the voice
  • N-grams
  • Spell correction
  • Tokenización
  • Classification (decision tree. Naïve Bayes)
  • Noun Phrase Extraction
  • Integration with WordNet

For more information, consult the official documentation: Link.


It is one of the most advanced natural language processing tools that exist today. This is based on PyTorch tools and libraries. It is ideal for commercial and research applications. It becomes an undeniable tool for a wide range of text research. AllenNLP uses the open source SpaCy library for data preprocessing and, at the same time, handles application cycles on its own. The fundamental component of AllenNLP is that it is easy to use. Unlike other NLP tools that have numerous modules, AllenNLP simplifies natural language processing. So you never feel lost in performance results. It is an amazing tool for beginners. AllenNLP's most stimulating model is Event2Mind. With this tool, can research the purpose and customer response, that are essential for the advancement of the item or service. AllenNLP is reasonable for both simple and complex tasks.


For more information, consult the official documentation: Link.


This marginally underperformed library is one of my best picks, as it offers a wide scope of analysis and a great inclusion of languages. Thanks to NumPy, also works very fast. Using multiple languages ​​is like spaCy: is competent, clear and, fundamentally, a great option for projects that include a language that spaCy doesn't comply with.

The following are the features of Polyglot:

  • Tokenización (165 Languages)
  • Language detection (196 Languages)
  • Named entity recognition (40 Languages)
  • Voice tagging part (16 Languages)
  • Sentiment analysis (136 Languages)
  • Word embeddings (137 Languages)
  • Morphological analysis (135 Languages)
  • Transliteration (69 Languages)

For more information, consult the official documentation: Link.


It is a large open library of natural language processing and the most used among data scientists for NLP tasks.. Provides a large number of algorithms for building machine learning models. It has excellent documentation that helps data scientists and makes learning easier. The main advantage of sci-kit learn is that it has great intuitive class methods. It offers many functions for the bag of words to convert tet to numeric vectors. It also has some downsides. Does not provide you with neural networks for text preprocessing. Better to use other NLP libraries if you want to do more complex preprocessing, like POS tagging for text corpus.


For more information, consult the official documentation: Link


Then, in this article, we have covered the 8 Top Natural Language Processing Libraries in Python for Machine Learning in 2021. Hope you learn something from this blog and it turns out better for your project. Thanks for reading and your patience. Good luck!

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