10 Best Python Libraries for Natural Language Processing (2022)

AI

Python is widely considered the best programming language, and it is critical for artificial intelligence (AI) and machine learning tasks. Python is an extremely efficient programming language when compared to other mainstream languages, and it is a great choice for beginners thanks to its English-like commands and syntax. Another one of the best aspects of the Python programming language is that it consists of a huge amount of open-source libraries, which make it useful for a wide range of tasks. 

Python and NLP

Natural language processing, or NLP, is a field of AI that aims to understand the semantics and connotations of natural human languages. The interdisciplinary field combines techniques from the fields of linguistics and computer science, which is used to create technologies like chatbots and digital assistants. 

There are many aspects that make Python a great programming language for NLP projects, including its simple syntax and transparent semantics. Developers can also access excellent support channels for integration with other languages and tools. 

Perhaps the best aspect of Python for NLP is that it provides developers with a wide range of NLP tools and libraries that allow them to handle a number of tasks, such as topic modeling, document classification, part-of-speech (POS) tagging, word vectors, sentiment analysis, and more. 

Let’s take a look at the 10 best Python libraries for natural language processing: 

1. Natural Language Toolkit (NLTK) 

Topping our list is Natural Language Toolkit (NLTK), which is widely considered the best Python library for NLP. NLTK is an essential library that supports tasks like classification, tagging, stemming, parsing, and semantic reasoning. It is often chosen by beginners looking to get involved in the fields of NLP and machine learning. 

NLTK is a highly versatile library, and it helps you create complex NLP functions. It provides you with a large set of algorithms to choose from for any particular problem. NLTK supports various languages, as well as named entities for multi language. 

Because NLTK is a string processing library, it takes strings as input and returns strings or lists of strings as output. 

Pros and Cons of using NLTK for NLP: 

  • Pros:
    • Most well-known NLP library
    • Third-party extensions
  • Cons: 
    • Learning curve
    • Slow at times
    • No neural network models
    • Only splits text by sentences

2. spaCy

SpaCy is an open-source NLP library explicitly designed for production usage. SpaCy enables developers to create applications that can process and understand huge volumes of text. The Python library is often used to build natural language understanding systems and information extraction systems. 

One of the other major benefits of spaCy is that it supports tokenization for more than 49 languages thanks to it being loaded with pre-trained statistical models and word vectors. Some of the top use cases for spaCy include search autocomplete, autocorrect, analyzing online reviews, extracting key topics, and much more.

Pros and Cons of using spaCy for NLP: 

  • Pros:
    • Fast
    • Easy to use
    • Great for beginner developers
    • Relies on neural networks for training models
  • Cons: 
    • Not as flexible as other libraries like NLTK

3. Gensim

Another top Python library for NLP is Gensim. Originally developed for topic modeling, the library is now used for a variety of NLP tasks, such as document indexing. Gensim relies on algorithms to process input larger than RAM. 

With its intuitive interfaces, Gensim achieves efficient multicore implementations of algorithms like Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Some of the library’s other top use cases include finding text similarity and converting words and documents to vectors. 

Pros and Cons of using Gensim for NLP: 

  • Pros:
    • Intuitive interface
    • Scalable
    • Efficient implementation of popular algorithms like LSA and LDA
  • Cons: 
    • Designed for unsupervised text modeling
    • Often needs to be used with other libraries like NLTK

5. CoreNLP 

Stanford CoreNLP is a library consisting of a variety of human language technology tools that help with the application of linguistic analysis tools to a piece of text. CoreNLP enables you to extract a wide range of text properties, such as named-entity recognition, part-of-speech tagging, and more with just a few lines of code. 

One of the unique aspects of CoreNLP is that it incorporates Stanford NLP tools like the parser, sentiment analysis, part-of-speech (POS) tagger, and named entity recognizer (NER). It supports five languages in total: English, Arabic, Chinese, German, French, and Spanish. 

Pros and Cons of using CoreNLP for NLP: 

  • Pros:
    • Easy to use
    • Combines various approaches 
    • Open source license
  • Cons: 
    • Outdated interface
    • Not as powerful as other libraries like spaCy

5. Pattern

Pattern is a great option for anyone looking for an all-in-one Python library for NLP. It is a multipurpose library that can handle NLP, data mining, network analysis, machine learning, and visualization. It includes modules for data mining from search engineers, Wikipedia, and social networks. 

Pattern is considered one of the most useful libraries for NLP tasks, providing features like finding superlatives and comparatives, as well as fact and opinion detection. These features help it stand out among other top libraries. 

Pros and Cons of using Pattern for NLP: 

  • Pros:
    • Data mining web services
    • Network analysis and visualization
  • Cons: 
    • Lacks optimization for some NLP tasks

6. TextBlob

A great option for developers looking to get started with NLP in Python, TextBlob provides a good preparation for NLTK. It has an easy-to-use interface that enables beginners to quickly learn basic NLP applications like sentiment analysis and noun phrase extraction. 

Another top application for TextBlob is translations, which is impressive given the complex nature of it. With that said, TextBlob inherits low performance form NLTK, and it shouldn’t be used for large scale production. 

Pros and Cons of using TextBlob for NLP: 

  • Pros:
    • Great for beginners
    • Provides groundwork for NLTK
    • Easy-to-use interface
  • Cons: 
    • Low performance inherited from NLTK
    • Not good for large scale production use

7. PyNLPI 

PyNLPI, which is pronounced as ‘pineapple,’ is one more Python library for NLP. It contains various custom-made Python modules for NLP tasks, and one of its top features is an extensive library for working with FoLiA XML (Format for Linguistic Annotation). 

Each one of the segregated modules and packages is useful for standard and advanced NLP tasks. Some of these tasks include extraction of n-grams, frequency lists, and building a simple or complex language model.

Pros and Cons of using PyNLPI for NLP: 

  • Pros:
    • Extraction of n-grams and other basic tasks
    • Modular structure
  • Cons: 

8. scikit-learn

Originally a third-party extension to the SciPy library, scikit-learn is now a standalone Python library on Github. It is utilized by big companies like Spotify, and there are many benefits to using it. For one, it is highly useful for classical machine learning algorithms, such as those for spam detection, image recognition, prediction-making, and customer segmentation. 

With that said, scikit-learn can also be used for NLP tasks like text classification, which is one of the most important tasks in supervised machine learning. Another top use case is sentiment analysis, which scikit-learn can help carry out to analyze opinions or feelings through data.

Pros and Cons of using PyNLPI for NLP: 

  • Pros:
    • Versatile with range of models and algorithms
    • Built on SciPy and NumPy
    • Proven record of real-life applications
  • Cons: 

9. Polyglot

Nearing the end of our list is Polyglot, which is an open-source python library used to perform different NLP operations. Based on Numpy, it is an incredibly fast library offering a large variety of dedicated commands. 

One of the reasons Polyglot is so useful for NLP is that it supports extensive multilingual applications. Its documentation shows that it supports tokenization for 165 languages, language detection for 196 languages, and part-of-speech tagging for 16 languages. 

Pros and Cons of using Polyglot for NLP: 

  • Pros:
    • Multilingual with close to 200 human languages in some tasks
    • Built on top of NumPy
  • Cons: 
    • Smaller community when compared to other libraries like NLTK and spaCy

10. PyTorch

Closing out our list of 10 best Python libraries for NLP is PyTorch, an open-source library created by Facebook’s AI research team in 2016. The name of the library is derived from Torch, which is a deep learning framework written in the Lua programming language. 

PyTorch enables you to carry out many tasks, and it is especially useful for deep learning applications like NLP and computer vision. 

Some of the best aspects of PyTorch include its high speed of execution, which it can achieve even when handling heavy graphs. It is also a flexible library, capable of operating on simplified processors or CPUs and GPUs. PyTorch has powerful APIs that enable you to expand on the library, as well as a natural language toolkit. 

Pros and Cons of using Pytorch for NLP: 

  • Pros:
    • Robust framework
    • Cloud platform and ecosystem
  • Cons: 
    • General machine learning toolkit
    • Requires in-depth knowledge of core NLP algorithms 

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