9 Tools and Libraries to Get You Going With Natural Language Processing
In previous posts, we’ve discussed what NLP is, the steps required to run NLP, and how NLP works together.
Now that you have a fundamental understanding of NLP and how it can help you scale your content marketing, you can start researching the tools and libraries you need to get started.
There are all kinds of tools out there for all types of NLP tasks. Some of them are open source tools, free to the public, and built by the contributions of volunteers. Others are premium, offered by big names in computing and data processing.
Even Google is in on the AI game.
But before we dive into a list of the tools and libraries out there, there are some terms you should know. You’ll see me reference Python, Java, and Node in this post. If you’re not a developer by trade, it helps to know what these are and what your development team generally works with.
Let’s define them briefly and then talk about the kinds of tools out there to help you get your NLP strategy up and running.
What Are Python, Java, and Node?
Python and Java are both open-source programming languages, and they’re both used to build AI and NLP applications. Python and Java are the number three and number two programming languages, respectively, according to GitHub, a software development host for open-source projects.
Open source refers to programming languages and frameworks that are free to use and relatively easy to manipulate. Your developers can customize open-source NLP tools and libraries to meet your brand’s needs.
But be warned that open source doesn’t always come with out-of-the-box solutions, which could mean a lot of development and testing before anything works.
Premium refers to the opposite. These are subscription-based tools and libraries. They generally offer more out-of-the-box options that you can plug into existing infrastructure, which can be helpful if you’re just starting with AI development, or you want to deploy something quickly.
Python Tools and Libraries for NLP
SpaCy tags itself as “industrial-strength natural language processing.” It’s a text analytics library that allows developers to tackle a variety of NLP projects. SpaCy supports over 52 languages and prides itself on its speed and accuracy of processing, including many features such as with many features, including named entity recognition and PoS tagging.
Natural Language Toolkit (NLTK)
NLTK is a well-known open-source NLP Python library. It provides all kinds of libraries to help with text processing and natural language understanding, including semantic analysis.
But the neat thing is its commitment to accessibility. The brains behind NLTK provide in-depth guides that teach the fundamentals of programming, so even beginners can start playing with NLP.
According to their site, their approach to NLP programming makes it a useful tool for researchers, students, and teachers.
If your team isn’t quite up-to-speed with programming in general, this might be a good place to start.
TensorFlow is an end-to-end platform for companies interested in machine learning and NLP. Written in python/C++, it’s completely open-source and comes with a variety of libraries and tools developers can use to build their own apps.
It integrates with frameworks like Keras and other high-level models to build neural networks easily and quickly.
Node Tools and Libraries for NLP
NLP.js can guess the language of the text it is analyzing — it has even been trained to recognize Klingon! This tool is great for unstructured data applications like translation and chatbots. It identifies 34 different languages and includes a natural language processing classifier and a natural language generation manager.
This tool is completely open-source and relies on the contributions of programmers around the world.
Java Tools and Libraries for NLP
According to their site, Apache OpenNLP is a volunteer-written, open-source tool for NLP. It “supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection, and coreference resolution.”
These processes allow developers to create apps that can break down parts of speech, spoken or written, and understand it.
The Stanford Natural Language Processing Group
Stanford has made some of its language processing tools, including their statistical, rule-based, and deep learning NLP tools, available to the public. There’s a whole list of core libraries and tools on their site, including libraries for tagging and parsing and tools for translation.
It’s important to note, however, that while their products are open-source, you have to contact Stanford for commercial licensing before using them in any proprietary tools.
Other NLP Tools
Amazon Deep Learning AMIs is a premium service that gives you the tools to run NLP no matter what programming language you use. It also works with several existing deep learning frameworks that we’ve mentioned, including Keras and TensorFlow.
You’ve probably heard of Watson at this point. That’s the AI machine that won Jeopardy!. But Watson offers tools and libraries for NLP, as well. You can download packages for Python, Node, or Java to build chatbots, perform sentiment analyses on social media or analyze online reviews, among other things.
Google Cloud Natural Language
Google Cloud has two options for natural language processing. The first is AutoML Natural Language, where developers upload existing documents to train the tool, then deploy it to perform several NLP tasks. It’s meant for developers who don’t have much experience with AI, deep learning or NLP.
Google also offers its Natural Language API, which allows more experienced developers to create and exist text analysis, sentiment analysis, and translation tools on their own.
Both exist in Google’s cloud.
For every kind of NLP need, there are tools and libraries to help you. What you choose will depend a lot on your dev team, so get them involved. You’ll need to know their familiarity with AI as well as with the three programming languages I talked about. You’ll also need to understand your tech stack and what it can support.
Ask the right questions of your dev team and make clear the needs of your organization. Then, you can choose the right natural language processing tools and libraries for the company as a whole.
Laurie is a freelance writer, editor, and content consultant and adjunct professor at Fisher College. Her work includes the development and execution of content strategies for B2B and B2C companies, including marketing and audience research, content calendar creation, hiring and managing writers and editors, and SEO optimization. You can connect with her on Twitter or LinkedIn.