A Guide to Top Natural Language Processing Libraries

Natural Language Processing is one of the hottest areas of research. While NLP tasks may seem a bit complicated at first, they can be made easier by using the right tools. This article covers a list of the top 6 NLP Libraries that can save you time and effort.

A Guide to Top Natural Language Processing Libraries
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Different Languages are used for communication purposes but it is considered one of the most complex data forms to work with. Have you ever thought that how voice assistants like Google Translate, Alexa, and Siri are able to understand, process, and respond to human commands? It is possible because of Natural Processing Language. NLP is the branch of data science that aims at making computers understand the semantics and analyze the textual data to extract meaningful insights from it. Some of the typical applications of Natural Language Processing are as follows:

  • Machine Translation
  • Text Summarization
  • Speech Recognition
  • Recommendation Systems
  • Sentiment Analysis
  • Market Intelligence

NLP libraries are built-in packages to incorporate NLP solutions into your application. Such libraries are really useful as they enable developers to focus on what really matters for the project. Below is an introduction to some of the most popular NLP Libraries that can be used to build intelligent applications.


1. NLTK - Natural Language Toolkit


GitHub Stars ⭐: 11.8k    Link to GitHub Repo: Natural Language Toolkit

NLTK is the most recognized Python library to process human language data. It provides an intuitive interface with over more than 50 corpora and lexical resources. It is a versatile and open-source library that supports tasks like classification, tokenization, POS tagging, stopping word removal, stemming, semantic reasoning, etc. 

Pros Cons
Comprehensive Steep Learning Curve
Large Community Support Can be slow & Memory Intensive
Extensive Documentation



Useful Resources



2. SpaCy


GitHub Stars ⭐: 25.7k    Link to GitHub Repo: SpaCy

SpaCy is an open-source library developed to be used in production environments. It can quickly process high volumes of text making it a perfect option for statistical NLP. It comes with up to 80 pre-trained pipelines for 24 languages and currently supports tokenization for 70+ languages. Besides facilitating tasks like POS tagging, Dependency Parsing, Sentence Boundary Detection, Named Entity Recognition, Text Classification, Rule-based Matching, etc it also provides a variety of linguistic annotations to give you insights into a text’s grammatical structure. Such features greatly enhance the accuracy and depth of the NLP Tasks.

Pros Cons
Fast & Efficient Supports limited languages as compared to NLTK
Pre-trained models  The size of some pre-trained models may be of concern to users with limited computing resources
Allows Model Customization



Useful Resources


  • SpaCy Online Documentation - Official Docs
  • SpaCy Online Courses - Advanced NLP with SpaCy
  • SpaCy Universe is a community-driven platform with tools, extensions, and plugins built on top of SpaCy. It also contains demos and books for guidance - SpaCy Universe


3. Gensim


GitHub Stars ⭐: 14.2k     Link to GitHub Repo: Gensim

Gensim is a Python library popularly known for topic modeling, document indexing, and similarity retrieval with large corpora. It offers pre-trained models for word embeddings that are used to identify the semantic similarity between the two documents. For instance, a pre-trained word2vec model can identify that “Paris” and “France” are related as Paris is the capital of France. The ability to identify such semantic relationships provides deep insights into the underlying meaning and context of data. The ability to process large inputs than the RAM available makes Gensim extremely effective.

Pros Cons
Intuitive Interface Limited PreProcessing Capabilities
Efficient and Scalable
Support for Distributed Computing Limited support for Deep Learning Models
Offers a wide range of Algorithms



Useful Resources



4. Stanford CoreNLP


GitHub Stars ⭐: 8.9k     Link to GitHub Repo: Stanford CoreNLP

Stanford CoreNLP is one of the well-tested Natural Language Processing tools written in Java. It takes the raw human language as the input and can perform a wide variety of operations like POS tagging, Named Entity Recognition, dependency parsing, and semantic analysis with just a few lines of code. Although it was originally designed for English, now it also supports numerous languages but is not limited to Arabic, French, German, Chinese, etc. Overall, it's a robust and reliable open-source tool for NLP tasks.

Pros Cons
High Accuracy Outdated Interface
Extensive Documentation Limited Scalability
Comprehensive Linguistic Analysis



Useful Resources



5. TextBlob


GitHub Stars ⭐: 8.5k     Link to GitHub Repo: TextBlob

TextBlob is another Python library used for processing textual data. It comes with an extremely friendly and easy-to-use interface. It provides a simple API to perform tasks like Noun phrase extraction, Part-of-speech tagging, Sentiment analysis, Tokenization, Word and phrase frequencies, Parsing, WordNet integration, etc. I would personally recommend this to entry-level programmers who want to acquaint themselves with NLP tasks.

Pros Cons
Beginner Friendly Slower Performance
Easy-to-use Interface Limited Features 
Integration with NLTK



Useful Resources



6. Hugging Face Transformers


GitHub Stars ⭐: 91.9k     Link to GitHub Repo: Hugging Face Transformers

Hugging Face Transformers is a powerful Python NLP Library with thousands of pre-trained models that can be used to perform NLP tasks. These models are trained on vast amounts of data and can understand the underlying patterns in the textual data. Using pre-trained models saves the time and resources of the developer as compared to training their own models from scratch. Transformer models can also perform tasks like table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.

Pros Cons
Easy to Use Resource Intensive
Large and Active Community Expensive cloud-based services
Language Support
Lower compute costs



Useful Resources





NLP libraries have played a significant role in accelerating the progress in NLP research. It has enabled machines to communicate effectively with humans. Although NLP tasks may seem a bit complicated at first with the right tools you can handle them really well. The above-mentioned list only refers to only the top libraries currently being used in NLP but there is much more out there that you can explore. I hope you learned something valuable from this article and I would really encourage you to try out these tools and build something cool. 
Kanwal Mehreen is an aspiring software developer with a keen interest in data science and applications of AI in medicine. Kanwal was selected as the Google Generation Scholar 2022 for the APAC region. Kanwal loves to share technical knowledge by writing articles on trending topics, and is passionate about improving the representation of women in tech industry.