Top 5 Python libraries for Natural language processing


Natural language processing (NLP) is a field that is an intersection of Data Science and Artificial Intelligence. It aims to understand the semantics and connotations of human language.

Its primary focus is on finding meaningful information from the text and the next step is to train the data models based on the acquired insights. NLP functions are widely used in text mining, text classification, text analysis, sentiment analysis, speech recognition, and machine translation.

This is all possible only because the wide range of NLP libraries are available in Python. The basic aim of all the libraries is to convert free text sentences into a structured feature correctly, also it must be able to implement the latest algorithms and models efficiently.

So, in this article, we are going to cover the top 5 natural language libraries and tools that are very useful in solving real-world problems.

Also read: Top Machine Learning libraries for Python

Top Natural language processing libraries for Python

Let’s now explore the 5 different NLP libraries that are available for Python and can be used for text generation and training. You can even use these to create chatbots in Python.

1. Natural language Toolkit (NLTK)

It is one of the important libraries for building Python programs that enables us to work with human language data and get insights from it.

It gives simple interfaces for over 50 corpora (a large collection of written or spoken texts that are used for language research) and lexical assets like WordNet.

NLTK also helps set up text pre-processing libraries for tagging, parsing, classification, stemming, tokenization and semantic reasoning wrappers for NLP libraries and active conversation discussion.

NLTK is free and open-source. Its easily accessible for Windows, Mac OS, and Linux. Due to the wide range of functionality, it is slow and sometimes it is difficult to match the demand of production usage.

Features of NLTK include Parts of speech tagging, Entity Extraction, Tokenization, Parsing, Semantic Reasoning, Stemming, and Text classification.


pip install nltk

2. Gensim

Gensim is a very popular library for natural language processing works. It has a special feature to identify semantic similarities between two documents by the use of vector space modelling. Its algorithms are memory-independent concerning corpus size which implies that we can easily process the input larger than RAM.

It is designed for “Topic Modelling, Document indexing, and similarity retrieval for large corpora (a large collection of written or spoken texts that are used for language research). It is broadly used in Data analysis, Text generation applications and Semantic search applications. It gives us the set of algorithms that are very important in natural language works.

Some algorithms for gensim are Hierarchical Dirichlet Process(HDP), Random Projections(RP), Latent Dirichlet Allocation(LDA), Latent Semantic Analysis(LSA/SVD/LSI), or word2vec deep learning. 


pip install — upgrade gensim

3. CoreNLP

Standford CoreNLP contains a grouping of human language technology tools. CoreNLP is meant to make the use of semantic analysis tools to a piece of text simple and proficient. With the help of CoreNLP, you can extract all kinds of text properties (like named-entity recognition, part-of-speech tagging, etc.) with only a few lines of code.  

Since CoreNLP is written in Java, it requires java to be installed on your device. However, it does offer a programming interface in many popular programming languages which include Python as well. It incorporates numerous of Standford’s NLP tools like the parser, sentiment analysis, bootstrapped pattern learning, named entity recognizer (NER), and coreference resolution system, part-of-speech (POS) tagger the name of a few.

Furthermore, CoreNLP supports four languages apart from English, Chinese, German, French, Spanish and Arabic.


pip install stanfordnlp

4. SpaCy

SpaCy is an open-source Python Natural language processing library. It is designed explicitly for production usage to solve real-world problems and it helps in handling a huge number of text data. It is equipped with pre-trained statistical models and word vectors and SpaCy is written in python in Cython (The Cython language is a superset of the Python language) which is why it is much faster and more efficient to handle a large amount of text data.

The top features of SpaCy are:

  • It provides multi-trained transformers like BERT.
  • 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 is way faster than other libraries.
  • It can preprocess text for Deep Learning.
  • It has 55 trained pipelines in more than 17 languages.

Installation (along with dependencies)

pip install –U setuptools wheel
pip install –U spacy
python -m spacy download en_core_web_sm

5. Pattern

Pattern is an extremely useful library in Python, that can be used to implement Natural Language processing tasks. It is open-sourced, and free for anyone to use. It can be used for NLP, text mining, web mining, network analysis, and Machine Learning.

It comes with a host of tools for Data mining (Google, Wikipedia API, a web crawler, and an HTML DOM Parser), NLP (n-gram search, sentiment analysis, WordNet, part-of-speech taggers), and ML (Vector space model, clustering, SVM) and network analysis with graph centrality and visualization.

It is a very powerful tool for a scientific and non-scientific audience. Its syntax is very easy and clear and the best part is the function names and parameters are chosen in a way so that the commands are self-explanatory also it serves as a rapid development framework for web developers.


pip install pattern


In this article, we went through the top 5 most commonly used python libraries for Natural language processing and we discussed when we have to use which library as per our requirement. I hope you learn something from this blog and it will turn out best for your project.


Further Reads

Latent Dirichlet Allocation (LDA) Algorithm in Python