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. This is also why machine learning is often part of NLP projects.
But why are so many organizations interested in NLP these days? Primarily because these technologies can provide them with a broad range valuable insights and solutions that address language-related problems consumers might experience when interacting with a product.
There’s a reason why tech giants like Google, Amazon, or Facebook are pouring millions of dollars into this line of research to power their chatbots, virtual assistants, recommendation engines, and other solutions powered by machine learning.
Since NLP relies on advanced computational skills, developers need the best available tools that help to make the most of NLP approaches and algorithms for creating services that can handle natural languages.
What is an NLP library?
In the past , only experts could be part of natural language processing projects that required superior knowledge of mathematics, machine learning, and linguistics. Now, developers can use ready-made tools that simplify text preprocessing so that they can concentrate on building machine learning models.
There are many tools and libraries created to solve NLP problems. Read on to learn more 8 amazing Python Natural Language Processing libraries that have over the years helped us deliver quality projects to our clients.
Why use Python for Natural Language Processing (NLP)?
There are many things about Python that make it a really good programming language choice for an NLP project. The simple syntax and transparent semantics of this language make it an excellent choice for projects that include Natural Language Processing tasks. Moreover, developers can enjoy excellent support for integration with other languages and tools that come in handy for techniques like machine learning.
But there’s something else about this versatile language that makes is such a great technology for helping machines process natural languages. It provides developers with an extensive collection of NLP tools and libraries that enable developers to handle a great number of NLP-related tasks such as document classification, topic modeling, part-of-speech (POS) tagging, word vectors, and sentiment analysis.
1. Natural Language Toolkit (NLTK)
NLTK is an essential library supports tasks such as classification, stemming, tagging, parsing, semantic reasoning, and tokenization in Python. It’s basically your main tool for natural language processing and machine learning. Today it serves as an educational foundation for Python developers who are dipping their toes in this field (and machine learning).
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, Python libraries, and other tools in their courses.
This library is pretty versatile, but we must admit that it’s also quite difficult to use for Natural Language Processing with Python. NLTK can be 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 Python 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. That’s why it can be useful for developers interested in trying their hand at natural language processing in Python. What is the greatest advantage of CoreNLP? 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 corpora 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. The tool’s vector space modeling capabilities are also top notch.
spaCy is a relatively young library was designed for production usage. That’s why it’s so much more accessible than other Python NLP libraries like 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 and efficient.
However, no tool is perfect. In comparison to the libraries we covered so far, spaCy supports the smallest number of languages (seven). However, the growing popularity of machine learning, NLP, and spaCy as a key library means that the tool might start supporting more programming languages 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 efficient, straightforward, and basically 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.
This handy NLP library provides developers with a wide range of algorithms for building machine learning models. It offers many functions for using the bag-of-words method of creating features to tackle text classification problems. The strength of this library is the intuitive classes methods. Also, scikit-learn has an excellent documentation that helps developers make the most of its features.
However, the library doesn’t use neural networks for text preprocessing. So if you’d like to carry out more complex preprocessing tasks like POS tagging for your text corpora, it’s better to use other NLP libraries and then return to scikit-learn for building your models.
Another gem in the NLP libraries Python developers use to handle natural languages. Pattern allows part-of-speech tagging, sentiment analysis, vector space modeling, SVM, clustering, n-gram search, and WordNet. You can take advantage of a DOM parser, a web crawler, as well as some useful APIs like Twitter or Facebook. Still, the tool is essentially a web miner and might not be enough for completing other natural language processing tasks.
Take advantage of Python for NLP
When it comes to natural language processing, Python is a top technology. Developing software that can handle natural languages in the context of artificial intelligence can be challenging. But thanks to this extensive toolkit and Python NLP libraries developers get all the support they need while building amazing tools.
These 8 libraries and the innate characteristics of this amazing programming language make it a top choice for any project that relies on machine understanding of human languages.
Thirsty for more knowledge? Check out our article about Python machine learning libraries for data science projects
Do you know any other useful 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.