Akshay Gupta — May 1, 2021
Beginner Libraries NLP Python Text Unstructured Data

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

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 the text. This is additionally why Artificial Intelligence is regularly essential for NLP projects.
So what’s the reason, why many companies care about NLP? Basically in light of the fact that 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 are going to cover the top 8 Natural Language Processing(NLP) libraries and tools that could be useful for build real-world projects. So let’s start!

 

Table Of Contents

  1. Natural Language Toolkit(NLTK)
  2. GenSim
  3. SpaCy
  4. CoreNLP
  5. TextBlob
  6. AllenNLP
  7. polyglot
  8. scikit-learn

 

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 official documentation: Link

GenSim

Gensim is a famous python library for natural language processing tasks. It provides a special feature to identify semantic similarity between two documents by the use of vector space modelling and the topic modelling toolkit. All algorithms in GenSim are memory-independent concerning corpus size it means we can process input larger than RAM. It provides a set of algorithms that are very useful in natural language tasks such as 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 official documentation: Link.

 

SpaCy

SpaCy is an open-source python Natural language processing library. It is mainly designed for production usage- to build real-world projects and it helps to handle a large number of text data. This toolkit is written in python in Cython which’s why it much faster and 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 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 Java be introduced on your device. Notwithstanding, it offers programming interfaces for some well-known programming languages, including Python. The tool consolidates various Stanford’s NLP tools like the sentiment analysis, part-of-speech (POS) tagger, bootstrapped pattern learning, parser, named entity recognizer (NER), coreference resolution system, to give some examples. Besides, CoreNLP upholds four dialects separated from English – Arabic, Chinese, German, French, and Spanish.

For more information, check official documentation: Link.

 

TextBlob

TextBlob is an open-source Natural Language Processing library in python (Python 2 and Python 3) powered by NLTK. 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 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 develops into an undeniable tool for a wide range of text investigation. AllenNLP utilizes 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. Not at all like other NLP tools that have numerous modules, AllenNLP makes the natural language process simple. 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 official documentation: Link.

 

Polyglot

This marginally lesser-realized library is one of my top choices since it offers an expansive scope of analysis and great language inclusion. On account of NumPy, it likewise works super quick. Utilizing multilingual is like spaCy – it’s proficient, clear, and fundamentally a fantastic choice for projects including a language spaCy doesn’t uphold.

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 a great open so natural language processing library and 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 it easier to learn. The main advantage of sci-kit learn is it has great intuitive class methods. It offers many functions for bag-of-words to convert tet 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 official documentation: Link

Conclusion

So in this article, we have covered the top 8 Natural Language Processing libraries in python for machine learning in 2021. I hope you learn something from this blog and it will turn out best for your project. Thanks for reading and your patience. Good luck!

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