10 Best Python Libraries for Natural Language Processing (2022)

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

Python and NLP

Natural language processing, or NLP, is an area 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 are used to create technologies such as chatbots and digital assistants.

Many aspects 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, classification of documents, 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)

At the top of the list is the Natural Language Toolkit (NLTK), which is widely considered the best Python library for NLP. NLTK is an essential library that supports tasks such as classification, markup, 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 very versatile library that helps you create complex NLP functions. It gives you a wide range of algorithms to choose from for any particular problem. NLTK supports various languages, as well as named entities for multiple languages.

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

Advantages and disadvantages of using NLTK for NLP:

  • Advantages:
    • The most famous NLP library
    • Third-party extensions
  • The inconvenients:
    • learning curve
    • Slow sometimes
    • No neural network model
    • Split text only by sentences

2. SpaCy

SpaCy is an open-source NLP library explicitly designed for production use. SpaCy enables developers to create applications capable of processing and understanding huge volumes of text. The Python library is often used to build natural language understanding systems and information retrieval systems.

Another major advantage of spaCy is that it supports tokenization for over 49 languages ​​by loading statistical models and pre-trained word vectors. Some of the main use cases for spaCy include semi-automatic search, auto-correction, analysis of online reviews, extraction of key topics, and much more.

Advantages and disadvantages of using spaCy for NLP:

  • Advantages:
    • Quick
    • Easy to use
    • Ideal for beginner developers
    • Leverages neural networks for training models
  • The inconvenients:
    • Not as flexible as other libraries like NLTK

3. Gensim

Gensim is another top Python library for NLP. 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 deal with inputs larger than RAM.

With its intuitive interfaces, Gensim achieves efficient multi-core implementations of algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Some of the other main use cases for the library include finding text similarities and converting words and documents into vectors.

Advantages and disadvantages of using Gensim for NLP:

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

5. NLP Core

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

One of the unique aspects of CoreNLP is that it integrates tools from Stanford NLP such as Analyzer, Sentiment Analysis, Part of Speech (POS) Marker and Named Entity Recognition System (NER ). It supports five languages ​​in total: English, Arabic, Chinese, German, French and Spanish.

Advantages and disadvantages of using CoreNLP for NLP:

  • Advantages:
    • Easy to use
    • Combines various approaches
    • open-source license
  • The inconvenients:
    • 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 versatile library that can handle NLP, data mining, network analysis, machine learning, and visualization. It includes data mining modules from research engineers, Wikipedia and social networks.

Pattern is considered one of the most useful libraries for NLP tasks, offering features such as finding superlatives and comparatives, as well as detecting facts and opinions. These features make it stand out from other leading libraries.

Advantages and disadvantages of using Pattern for NLP:

  • Advantages:
    • Data mining web services
    • Network analysis and visualization
  • The inconvenients:
    • Lack of optimization for some NLP tasks

6. TextBlob

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

Another major application for TextBlob is translation, which is impressive given its complex nature. That said, TextBlob inherits from poorly performing NLTK and should not be used for large scale production.

Advantages and disadvantages of using TextBlob for NLP:

  • Advantages:
    • Ideal for beginners
    • Provides basics for NLTK
    • Easy to use interface
  • The inconvenients:
    • Low performance inherited from NLTK
    • Not good for large scale production use

7. PyNLPI

PyNLPI, which is pronounced like “pineapple”, is another Python library for NLP. It contains various custom Python modules for NLP tasks, and one of its main features is a comprehensive library for working with FoLiA XML (Format for Linguistic Annotation).

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

Advantages and disadvantages of using PyNLPI for NLP:

  • Advantages:
    • Extracting n-grams and other basic tasks
    • Modular structure
  • The inconvenients:

8. scikit-learn

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

That said, scikit-learn can also be used for NLP tasks such as text classification, which is one of the most important tasks in supervised machine learning. Another major use case is sentiment analysis, which scikit-learn can help analyze opinions or sentiments through data.

Advantages and disadvantages of using PyNLPI for NLP:

  • Advantages:
    • Versatile with a range of models and algorithms
    • Built on SciPy and NumPy
    • Proven record of real world applications
  • The inconvenients:

9. Polyglot

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

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

Advantages and disadvantages of using Polyglot for NLP:

  • Advantages:
    • Multilingual with nearly 200 human languages ​​in some tasks
    • Built on NumPy
  • The inconvenients:
    • Small community compared to other libraries like NLTK and spaCy

10.PyTorch

Closing our list of top 10 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 framework deep learning tool written in the Lua programming language. .

PyTorch lets you perform many tasks, and it’s especially useful for deep learning applications like NLP and computer vision.

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

Advantages and disadvantages of using Pytorch for NLP:

  • Advantages:
    • Sturdy frame
    • Cloud platform and ecosystem
  • The inconvenients:
    • General Machine Learning Toolkit
    • Requires in-depth knowledge of basic NLP algorithms

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