Top 6 Python Libraries for Natural Language Processing in 2023

Natural Language Processing (NLP) combines data science and artificial intelligence to recognize and interpret human language. Today many smart devices understand and reciprocate in human language using NLP tools. Python libraries for Natural language processing are the most used resources in artificial intelligence to match human intelligence.

Most people who learn machine learning and artificial intelligence focus on choosing projects on Natural Language Processing with Python. NLP is slowly finding its applications in various industries and enterprises. NLP's most vital task is extracting valuable information to develop numerous machine learning and deep learning models.

Six Most Useful Python Libraries for Natural Language Processing

Python is an excellent choice for Natural Language Processing projects because of its simple syntax and many other reasons. It supports developers in blending with other languages and tools for machine learning techniques. With Python’s extensive library, performing NLP tasks such as document classification, tagging, stemming, and sentiment analysis is easier. Let’s learn about popular Python libraries for Natural Language Processing used to build real-world projects:-

Natural Language Tool Kit (NLKT)

NLTK is an open-source library for Python projects. It works on Windows, Linux, and macOS. It supports essential NLP tasks such as classification, tagging, stemming, tokenization, and parsing. It is the foundation for Python developers in machine learning. Steven Bird and Edward Loper developed the NLKT library at the University of Pennsylvania. This library is comparably slow and might not be a good choice for production usage.

GenSim

It is a popular Python library for Natural Language Processing tasks. It has a specialized feature identifying semantic similarities between two documents using vector space modeling and a topic modeling tool kit. It processes larger inputs using algorithms such as Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA). It has a user-friendly interface. This library is the best choice for converting similar documents into vectors.

spaCy

spaCy is an open-source Python NLP library. It is designed mainly for production usage. This tool kit is written in Cython, making it faster and more efficient. It supports more than 50 languages. It offers pre-trained statistical models and word vectors. It features components such as named entity recognition, dependency parsing, sentence segmentation, part-of-speech tagging, lemmatization, and Entity linking. It is easy to learn for beginners.

CoreNLP

CoreNLP consists of several human language technology tools. It is written in Java and was developed at Stanford University. It integrates well with other programming languages. It is designed for a production environment and works really fast. It supports five human languages: English, Spanish, French, Arabic, and Chinese. It includes various NLP tools such as the parser, sentiment analysis, named entity recognizer, conference resolution system, etc.

Polyglot

Polyglot is a lesser-known Python library; it offers a wide range of analyses and the inclusion of more languages. It is based on NumPy, which makes it work efficiently and clearly. It is an amazing choice for projects that include languages spaCy doesn’t support. It is the most multilingual library supporting around 200 human languages. Features of Polyglot include tokenization, language detection, sentiment analysis, word embeddings, morphological analysis, transliteration, etc.

TextBlob

TextBlob is an open-source Python library supported by NLTK. This library is designed mainly for processing textual data. It is the fastest among all the NLP libraries. It offers a user-friendly interface for beginners. Some NLP functionalities include part-of-speech tagging, noun phrase extraction, language translation, N-grams, wordnet integration, word reflection, etc. TextBlob objects are treated as Python strings which are pre-trained in NLP. Its main drawback is the lack of a neural network model.

End Note:

We hope this article has made you understand Python libraries for NLP. Using Python has accelerated the development of natural language processing systems in artificial intelligence. Choose the Python library that best suits your NLP project.

If you want to learn more about Natural language processing in artificial intelligence, you can enroll in the Data Science and Artificial Intelligence Program, which helps you to handle artificial intelligence projects from various domains.