NLP with Python: Top Python Libraries for Natural Language Processing

As the name suggests, rule-based NLP uses general rules as its primary data source. Here, we’re basically discussing common sense and laws of nature, such as how temperature affects our health and how to avoid certain situations in order not to get hurt.

It’s possible for an AI to internalize these rules and act accordingly, but it’s important to note that this type of processing takes more time as well as more manual input.

As a result, this kind of NLP is somewhat more flexible and future-proof. The knowledge and understanding of language allow tasks to be carried out in a much more precise manner, but it does call for more expertise.

On the other hand, statistical NLP mostly works based on a large amount of data. This is the type you’re likely to be more familiar with, since this is where machine learning and big data are most commonly used.

After some training, a statistics-based NLP model will be able to work out a lot on its own without external help. This makes it the faster of the two alternatives, as it can basically learn on its own, but keep in mind that you’ll need to have access to a really vast pool of data for it to work.

Still, since it only processes the data we feed it, rather than internalizing the same logic humans run on, it won’t be able to understand the context and other nuances as well as rule-based NLP would.

As we’ve already established, the programming languages we use to communicate with machines are based on strict logic. We’ve worked very hard to ensure that computers do exactly what we tell them to do, which is why their language is very precise.

Now, humans are the opposite of precise. Human languages have their rules and structures that are subject to the cultures in which they were developed. We use phrases, synonyms, and metaphors to say things that are sometimes the exact opposite of what the words said normally mean.

What’s more, the same sentence can have a completely different meaning when used by a different social group. This lack of precision is a deeply human trait of language, but in the end, it’s also the thing that makes us so hard to understand for machines.

Tone is another aspect that can be difficult for machines to read. We often use abstract terms, sarcasm, and other elements that rely on the other speaker knowing the context. Sometimes, the same word said in a different tone of voice can have an entirely different meaning.

This is why raw data cannot really supply machines with the information they need to understand us, as it takes years for us to learn the various social cues that help us understand each other.

Technology evolves very fast—but is it fast enough to catch up with our language? Many of us think of languages as monolithic, but that couldn’t be further from the truth. Language is constantly evolving, sometimes dramatically and sometimes so gradually that we don’t even see the transformation happening before our very eyes. That’s why it’s important for the future of NLP that the technology is as adaptable to the changes in language as we are, if not more.

If you ever google “Python NLP libraries,” NLTK is pretty much the first option that pops up on every list. False advertising? Not at all. NLTK is unquestionably your go-to Python library for NLP.

This thing has all the functions of a good NLP library: tagging, parsing, stemming, classification—you name it. Even though it’s relatively complex and takes a while to wrap your head around, it’s still very frequently used by beginners.

Most importantly, NLTK is incredibly versatile. It supports such a great deal of languages, and it has so many algorithms to choose from that you’re bound to find everything you need there.

And of course, since it’s by far the most popular Python NLP library, it has the most third-party extensions out there in case you need even more versatility.

Another extensively used open-source library is spaCy. It was designed with production in mind, allowing its users to make apps that can quickly parse large amounts of text. This makes it perfect for statistical NLP, due to the great amount of data required for it to function.

Even if it may not be as flexible as other libraries, spaCy’s so simple to use that even absolute beginners won’t have a hard time learning the ins and outs of it. It supports tokenization for 50+ languages, with word vectors and statistical models, which makes it the perfect tool for autocorrect, autocomplete, extracting key topics, etc.

TextBlob may not be the most robust tool on the market, and it may not be enough for larger projects, but it has the undeniable advantage of being the perfect entry-level NLP library.

With an incredibly friendly UI, TextBlob helps developers get acquainted with the world of NLP apps. If you’re looking for the best place to learn what noun phrase extraction or sentiment analysis even are, TextBlob is for you.

Along with NLTK, one of the most commonly used NLP libraries is Gensim. While it used to have a much more specific use, with topic modeling being its focus, nowadays it’s a tool that can help out with pretty much any NLP task. It’s important to remember, however, that it was originally designed for unsupervised text modeling.

Gensim is extremely effective because it can process inputs larger than the available RAM using algorithms like LS and LDA. Its UI is also very intuitive, making it a friendly library for those who aren’t too used to more pragmatic-looking systems.

If you’re looking for a tool that will help you quickly fish out text similarities or convert documents to vectors, this is your pick. Just keep in mind that you may need to use it alongside another library to get the full experience.

Developed at Stanford, this Java-based library is one of the fastest out there. CoreNLP can help you extract a whole bunch of text properties, including named-entity recognition, with relatively little effort. It’s one of the easiest libraries out there and it allows you to use a variety of methods for effective outcomes.

CoreNLP supports five languages and it utilizes most of the important NLP tools, such as apser, POS tagger, etc. However, it is worth noting that the UI is a bit on the dated side, so that can be quite a shock to someone with more modern taste.

Pattern is quite the comprehensive NLP library. It has pretty much everything you need: sentiment analysis, SVM, clustering, WordNet, POS tagging, DOM parsers, web crawlers, and many others. It’s an incredibly versatile tool that can also be used for data mining and visualization.

Additionally, it has quite a bit of features that set it apart from other NLP libraries, such as the ability to differentiate facts from opinions or find comparatives and superlatives. Do keep in mind, though, that the optimization maybe isn’t distributed evenly enough between all of its components.

When you’re working in a language that spaCy doesn’t support, polyglot is the ideal replacement because it performs many of the same functions as spaCy. In fact, the name really isn’t an exaggeration, as this library supports around 200 human languages, making it the most multilingual library on our list.

Furthermore, because it’s based on NumPy, polyglot’s quite fast. Unfortunately, not enough people have turned their eyes toward polyglot, since the community still isn’t as large as NLTK’s. We believe it will get there eventually, though.

The name admittedly looks very weird, but apparently, it’s supposed to be pronounced “pineapple.” Oddities aside, PyNLPI is a very interesting option, as it’s one of the few modular NLP libraries out there. It comes with a bunch of custom-made Python modules that are perfect for handling NLP tasks, including a FoLiA XML library.

Even if you haven’t heard of scikit-learn—or SciPy, for that matter, which scikit-learn originally splintered off from—you’ve definitely heard of Spotify. The popular digital music service works off scikit-learn, using its machine learning algorithms, spam detection functions, as well as other elements to bring us a very well-crafted app.

But that is by no means the only way scikit-learn can be used. It’s an incredibly versatile library, capable of text classification, supervised machine learning, and sentiment analysis—among others. While the limited support for deep learning may be a turn-off for some, it’s definitely a tool that’s proved reliable time and time again.

Finally, we reach PyTorch—an open-source library brought to us by the Facebook AI research team in 2016. Even though it’s one of the least accessible libraries on this list and requires some prior knowledge of NLP, it’s still an incredibly robust tool that can help you get results if you know what you’re doing.

It’s pretty much your best option if you want to look into deep learning. It’s also simply very fast. With PyTorch, you can be sure that everything will be processed quickly even if you’re working with visually complex data.