Natural language processing (NLP) is a field located at the intersection of data science and Artificial Intelligence (AI) that – when boiled down to the basics – is all about teaching machines how to understand human languages and extract meaning from text.
Why are so many organizations interested in NLP these days? Primarily because NLP technologies can provide them with a broad range valuable insights and solutions that address language-related problems consumer might experience when interacting with a product.
There’s a reason why tech giants like Google, Amazon, or Facebook are pouring millions into NLP research to power their chatbots and virtual assistants.
Since NLP relies on computational skills, developers need the right tools that help them make use of approaches and algorithms in their job of creating services that can handle human language.
Why use Python for Natural Language Processing (NLP)?
There are many things about Python that make it a top programming language for an NLP project. Python’s syntax and semantics are transparent, making it an excellent choice for Natural Language Processing. Moreover, it’s simple and offers excellent support for integration with other languages and tools.
But it also provides developers with extensive NLP tools and libraries that handle many NLP-related tasks such as document classification, topic modeling, part-of-speech (POS) tagging, and sentiment analysis.
Read on to learn more 6 amazing Python Natural Language Processing libraries that have over the years helped us deliver quality projects to our clients.
1. Natural Language Toolkit (NLTK)
Supporting tasks such as classification, tokenization, stemming, tagging, parsing, and semantic reasoning, this library is your main tool for natural language processing. Today it serves as an educational foundation for Python developers who are dipping their toes in NLP. The library was developed by Steven Bird and Edward Loper at the University of Pennsylvania and played a key role in breakthrough NLP research. Many universities around the globe now use NLTK in their courses.
This library is pretty versatile, but we must admit that it’s also quite difficult to use. Most of the time, it’s rather slow and doesn’t match the demands of quick-paced production usage. The learning curve is steep, but developers can take advantage of resources like this helpful book to learn more about the concepts behind the language processing tasks this toolkit supports.
TextBlob is a must for developers who are starting their journey with NLP in Python and want to make the most of their first encounter with NLTK. It basically provides beginners with an easy interface to help them learn most basic NLP tasks like sentiment analysis, pos-tagging, or noun phrase extraction.
We believe anyone who wants to make their first steps toward NLP with Python should use this library. It’s very helpful in designing prototypes. However, it also inherited the main flaws of NLTK – it’s just too slow to help developers who face the demands of NLP production usage.
This library was developed at Stanford University and it’s written in Java. Still, it’s equipped with wrappers for many different languages, including Python, so it comes in handy to Python developers interested in building NLP functionalities. The library is really fast and works well in product development environments. Moreover, some of CoreNLP components can be integrated with NLTK which is bound to boost the efficiency of the latter.
Gensim is a Python library that specializes in identifying semantic similarity between two documents through vector space modeling and topic modeling toolkit. It can handle large text collections with the help of efficiency data streaming and incremental algorithms, which is more than we can say about other packages that only target batch and in-memory processing. What we love about it is its incredible memory usage optimization and processing speed. These were achieved with the help of another Python library, NumPy.
This relatively young library was designed for production usage – that’s why it’s so much more accessible than NLTK. spaCy offers the fastest syntactic parser available on the market today. Moreover, since the toolkit is written in Cython it’s also really speedy. In comparison to the libraries we covered so far, it supports the smallest number of languages (seven). However, its growing popularity means that it might start supporting more of them soon.
This slightly lesser-known library is one of our favorites because it offers a broad range of analysis and impressive language coverage. Thanks to NumPy, it also works really fast. Using polyglot is similar to spaCy – it’s very straightforward and will be an excellent choice for projects involving a language spaCy doesn’t support. The library stands out from the crowd also because it requests the usage of a dedicated command in the command line through the pipeline mechanisms. Definitely worth a try.
Take advantage of Python for NLP
Developing software that can handle natural language can be challenging. But thanks to Python’s extensive toolkit developers get all the support they need while building amazing tools.
These 6 libraries and Python’s innate characteristics make it a top choice for any NLP project.
Thirsty for more knowledge? Check out our article about Python machine learning libraries for data science projects.
Do you know any other amazing Python NLP libraries? Or perhaps you’d like to know something more about one of the libraries covered in this post?
We invite you to share your experience and ask questions in comments to help everyone learn more about the best practices for developing NLP-powered software.