Top 8 Python Libraries For Natural Language Processing (NLP) in 2024

Introduction

Natural language processing (NLP) is a field situated at the convergence of data science and Artificial Intelligence (AI) that – when reduced to the basics is all about teaching machines how to comprehend human dialects and extract significance from text processing. This explains why Artificial Intelligence is essential for NLP projects.

You might be wondering – what’s the reason why many companies care about NLP? Basically, because these advances can give them an expansive reach, important bits of knowledge, and arrangements that address language-related issues purchasers may encounter while cooperating with an item.

So, in this article, we will cover the top 8 Python libraries for NLP and tools that could be useful for building real-world projects. Read on!

Learning Objectives

1. Understanding the Role of NLP in AI and Data Science:
   • Gain insight into the significance of Natural Language Processing (NLP).
   • Comprehend the basic principles of teaching machines to understand human languages and extract meaning from text processing.
   • Recognize the importance of AI in NLP projects for providing broad reach, valuable insights, and solutions to language-related issues.
2. Exploring Key NLP Libraries and Tools:
   • Familiarize yourself with prominent Python libraries and tools for NLP.
   • Understand the features and capabilities offered by each library/tool in terms of text processing, analysis, and machine learning applications.

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

Natural Language Toolkit (NLTK)

NLTK is the main library for building Python projects to work with human language data. It gives simple to-utilize interfaces to more than 50 corpora and lexical assets like WordNet, alongside a set-up of text preprocessing libraries for tagging, parsing, classification, stemming, tokenization, and semantic reasoning wrappers for NLP libraries and an active conversation discussion. NLTK is accessible for Windows, Mac OS, and Linux. The best part is that NLTK is a free, open-source, local area-driven venture. It has some disadvantages as well. It is slow and difficult to match the demands of production usage. The learning curve is somehow steep. Some of the features provided by NLTK are:

• Entity Extraction
• Part-of-speech tagging
• Tokenization
• Parsing
• Semantic reasoning
• Stemming
• Text classification

For more information, check the official documentation: Link

Gensim

Gensim is one of the best Python libraries for NLP tasks. It provides a special feature to identify semantic similarity between two documents using vector space modeling and the topic modeling toolkit. All algorithms in GenSim are memory-independent concerning corpus size, which means we can process input larger than RAM. It provides a set of algorithms that are very useful in natural language tasks such as the Hierarchical Dirichlet Process(HDP), Random Projections(RP), Latent Dirichlet Allocation(LDA), Latent Semantic Analysis(LSA/SVD/LSI) or word2vec deep learning. The most advanced feature of GenSim is its processing speed and fantastic memory usage optimization. The main uses of GenSim include Data Analysis, Text generation applications (chatbots), and Semantic search applications. GenSim highly depends on SciPy and NumPy for scientific computing.

For more information, check the official documentation: Link.

SpaCy

SpaCy is one of the best open-source Python libraries for NLP. It is mainly designed for production usage- to build real-world projects and helps handle a large number of text data. Renowned for its rapidity and precision, SpaCy is a favored option for handling extensive datasets. This toolkit is written in Python in Cython, which is why it is much faster and more efficient to handle a large amount of text data. Some of the features of SpaCy are shown below:

• It provides multi trained transformers like BERT.
• It is way faster than other libraries.
• Provides tokenization that is motivated linguistically In more than 49 languages.
• Provides functionalities such as text classification, sentence segmentation, lemmatization, part-of-speech tagging, named entity recognition, and many more.
• It has 55 trained pipelines in more than 17 languages. 

For more information, check the official documentation: Link.

CoreNLP

Stanford CoreNLP contains a grouping of human language innovation instruments. It means to make the use of semantic analysis tools to a piece of text simple and proficient. With CoreNLP, you can extract a wide range of text properties (like part-of-speech tagging,named-entity recognition, and so forth) in a couple of lines of code.

Since CoreNLP is written in Java, it requests that you introduce Java on your device. Notwithstanding, it offers programming interfaces for well-known programming languages, including Python. The tool consolidates various Stanford’s NLP tools, such as sentiment analysis, part-of-speech (POS) tagger, bootstrapped pattern learning, parser, named entity recognizer (NER), and coreference resolution system. Besides, CoreNLP upholds four dialects separate from English: Arabic, Chinese, German, French, and Spanish.

For more information, check the official documentation: Link.

TextBlob

TextBlob is one of the famous Python libraries for NLP (Python 2 and Python 3) powered by NLTK. Constructed atop NLTK, it furnishes a streamlined API for typical Natural Language Processing (NLP) functions. It is the fastest NLP tool among all the libraries. It is beginners friendly. It is a must-learning tool for data scientist enthusiasts who are starting their journey with Python and NLP. It provides an easy interface to help beginners and has all the basic NLP functionalities, such as sentiment analysis, phrase extraction, parsing, and many more. Some of the features of TextBlob are shown below:

• Sentiment analysis
• Parsing
• Word and phrase frequencies
• Part-of-speech tagging
• N-grams
• Spelling correction
• Tokenization
• Classification( Decision tree. Naïve Bayes)
• Noun phrase extraction
• WordNet integration

For more information, check the official documentation: Link.

AllenNLP

It is one of the most advanced Natural Language Processing Tools out there now. This is built on PyTorch tools and libraries. It is ideal for business and research applications. It has developed into an undeniable tool for various text-processing investigations. AllenNLP utilizes the SpaCy open-source library for data preprocessing while at the same time dealing with the lay cycles all alone. The fundamental component of AllenNLP is that it is easy to utilize. Unlike other NLP tools with numerous modules, AllenNLP simplifies the Natural Language Process. So you never feel lost in the yield results. It is an astounding tool for beginners. The most energizing model of AllenNLP is Event2Mind. With this tool, you can investigate client purpose and response, which are fundamental for item or service advancement. AllenNLP is reasonable for both straightforward and complex tasks.

For more information, check the official documentation: Link.

Polyglot

This marginally lesser-realized library, coupled with its expansive scope of analysis and excellent language coverage, is one of my top picks. Thanks to NumPy, it also performs super fast. Utilizing multilingual features akin to spaCy, it’s efficient and clear, making it an excellent choice for projects involving languages spaCy doesn’t support, especially when working with Python NLP libraries.

Following are the features of Polyglot:

• Tokenization (165 Languages)
• Language detection (196 Languages)
• Named Entity Recognition (40 Languages)
• Part of Speech Tagging (16 Languages)
• Sentiment Analysis (136 Languages)
• Word Embeddings (137 Languages)
• Morphological analysis (135 Languages)
• Transliteration (69 Languages)

For more information, check official documentation: Link.

Scikit-Learn

It is one of the greater Python libraries for NLP and is most used among data scientists for NLP tasks. It provides a large number of algorithms to build machine learning models. It has excellent documentation that helps data scientists and makes learning easier. The main advantage of sci-kit learn is it has great intuitive class methods. It offers many functions for bag-of-words to convert text into numerical vectors. It has some disadvantages as well. It doesn’t provide you with neural networks for text preprocessing. It is better to use other NLP libraries if you want to carry out more complex preprocessing, such as POS tagging for text corpora.

For more information, check the official documentation: Link

Conclusion

This article has provided an overview of the top 8 Natural Language Processing libraries in Python for machine learning in 2024. Whether you are a seasoned practitioner or a beginner in the field, these libraries offer a range of tools and functionalities to enhance your NLP projects. Additionally, it’s crucial to emphasize the importance of effective visualization‌ techniques to gain insights from your data and the utilization of word vectors for tasks like semantic similarity and language understanding. We hope the information presented here proves valuable to your projects. Thank you for reading, and best of luck with your endeavors in the dynamic world of Natural Language Processing and machine learning!

Check the NLP tutorial here: NLP Tutorials Part -I from Basics to Advance.

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Key Takeaways

1. NLTK: A versatile Python library with over 50 corpora and lexical resources, providing essential features like entity extraction, part-of-speech tagging, and text classification, but it may have challenges in production usage due to its slowness.
2. Gensim: A popular library for NLP tasks, offering semantic similarity identification and advanced algorithms like Latent Dirichlet Allocation (LDA) and word2vec deep learning, known for its efficient memory usage and processing speed.
3. SpaCy: An open-source Python library designed for production use, featuring speed and precision, providing functionalities such as tokenization, text classification, and named entity recognition in over 49 languages with efficient handling of large text datasets.
4. CoreNLP: Written in Java but accessible in Python, Stanford CoreNLP incorporates various NLP tools, including sentiment analysis, part-of-speech tagging, and named entity recognition, making semantic analysis tasks easy and efficient.
5. TextBlob: An open-source NLP library in Python powered by NLTK, offering a user-friendly API for common NLP functions, suitable for beginners with features like sentiment analysis, word frequency, part-of-speech tagging, and easy integration with NLTK.

Frequently Asked Questions (FAQs)

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Akshay