NLP with Python: Top Python Libraries for Natural Language Processing

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However you look at it, we’re coexisting with machines now. Although we’re still a long way from The Matrix (hopefully?), we’ve already stepped into areas that were only science fiction a decade ago.

Compared to us, computer programs, algorithms, and apps work on a whole different level due to their substantial complexity that’s only increasing with time. Yet we are happy to completely surround ourselves with them because they make our lives incomprehensibly easier. How is that even possible?

This is thanks to natural language processing (NLP)—the ability of computer programs to understand human language as it’s spoken and written. Even though we may never understand what an AI is thinking, with NLP we can now build a machine that uses language just like we humans do.

As the most widespread programming language in the world, Python is no stranger to natural language processing. In fact, there is a wide variety of excellent Python libraries that NLP engineers can take advantage of.

Read our article to discover what natural language processing is all about, what are its main challenges, and which top NLP libraries are actually worth your time!

What is NLP?

We use computers every day because they’re designed in a way that makes them good at something we’re not: calculating things extremely fast. As a result, machines are excellent when it comes to interpreting tabular data, i.e. spreadsheets. That’s why we use tools that work with data in this format whenever we wish to program something. It makes it more readable for the machine.

However, as the internet developed to the point where computers began searching the web for artificial data to improve communication with us, a problem has occurred. Humans don’t communicate using spreadsheets; instead, we construct phrases that are often very far from being organized. We don’t always behave logically, and yet that’s the only way computers usually know how to communicate.

In comes natural language processing. To put it in simple terms, NLP is an aspect of AI that aims at making machines understand human communication. Through NLP, computers can sort through what is normally meaningless jumbles of text and transform it into something that will make sense to them. This is achieved through machine learning and deep learning algorithms.

NLP can thus be thought of as an umbrella term for a variety of AI system functions, including name entity recognition, speech recognition, machine translation, spam detection, autocomplete, and predictive typing.

You’ll probably notice all of these are familiar systems we use on a regular basis, mostly through our phones. As a result, NLP has now become something ingrained in our everyday lives without us even noticing.

Rule-based NLP and statistics-based NLP

When it comes to natural language processing, there are two main approaches: rule-based and statistical. These general terms cover the type of data a specific system will use to process tasks.

Rule-based NLP

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.

Statistical NLP

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.

What are the main challenges of NLP?

It’s remarkable that we have computers that can understand human language these days. Having said that, it’s important to remember that NLP is still an emerging technology. Language is infinitely complex and ever-changing, so it will still be a long time until NLP truly reaches its full potential.

The main challenges that NLP is facing nowadays can be boiled down to three factors:

1. A fundamental difference in precision

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.

2. Ambiguity of the human language

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.

3. Keeping up with the changes

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.

What are NLP libraries?

This may all sound incredibly complex, but that’s just how things will be in the future. Welcome to web 2.0, where there are no gatekeepers and everyone has access to the information they require.

Although it may still appear that only professionals can benefit from AI, today any developer with a clever concept may use NLP even without decades’ worth of education.

Python is a versatile programming language for helping machines process natural language that also provides developers with an extensive collection of NLP tools.

With it, you get access to a number of ready-made libraries that can make things a lot easier for you. Libraries pretty much get most of the work out of the way, so that you and your developers can focus on what really matters for your project.

Top Python NLP libraries

Python is also very popular, so it offers an incredibly wide range of tools you could potentially employ. That’s why we’ve narrowed it down to a handy list of ten NLP libraries for you to use. Check it out!

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.

Final thoughts on Python libraries for NLP

Python is the best programming language out there when it comes to not only NLP, but other numerous areas of technology or business, as well. However, developing software that can handle natural languages in the context of artificial intelligence can still be quite challenging.

We hope that our article has helped you understand that with the right tools, natural language processing isn’t as complicated as it might first appear to be. And with these top 10 libraries we’ve listed, you’re pretty well set to go and take advantage of everything NLP has to offer!

Hungry for more? Check out these other resources you might find helpful:

In today’s fast-paced tech world, it can be really difficult to stay up-to-date with all the news and changes in the industry. So follow our blog to save yourself a ton of extra research work and stay on top of things!

And in case you have any questions on how to optimize your processes by applying natural language processing, computer vision, or recommendation algorithms—contact us! We’d be happy to help you reach your business goals.

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