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What’s the Difference Between NLP, NLU, and NLG?

8 min read

Artificial intelligence is changing the way we plan and create content. It’s also changing how users discover content, from what they search for on Google to what they binge-watch on Netflix.

More importantly, for content marketers, it’s allowing teams to scale by automating certain kinds of content creation and analyze existing content to improve what you’re offering and better match user intent.

There are many moving parts in the AI and machine learning process. Three you probably hear about a lot are natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG).

These terms are often confused because they’re all part of the singular process of reproducing human communication in computers.

Let’s take a look at what these terms mean and how they (and AI as a whole) can help marketers streamline their content marketing strategy.

What Is Natural Language Processing (NLP)?

Speech recognition is an integral component of NLP, which incorporates AI and machine learning. Here, NLP algorithms are used to understand natural speech in order to carry out commands. 

Natural language processing starts with a library, a pre-programmed set of algorithms that plug into a system using an API, or application programming interface. Basically, the library gives a computer or system a set of rules and definitions for natural language as a foundation.

Your development team can customize that base to meet the needs of your product.

Neural networks figure prominently in NLP systems and are used in text classification, question answering, sentiment analysis, and other areas. Processing big data involved with understanding the spoken language is comparatively easier and the nets can be trained to deal with uncertainty, without explicit programming.

So, if you’re Google, you’re using natural language processing to break down human language and better understand the true meaning behind a search query or sentence in an email. You’re also using it to analyze blog posts to match content to known search queries. 

NLP is also used whenever you ask Alexa, Siri, Google, or Cortana a question, and anytime you use a chatbot. The program is analyzing your language against thousands of other similar queries to give you the best search results or answer to your question.

The beautiful thing about AI and machine learning is that, with regular use, it learns your language patterns to improve and tailor its results.

What Is Natural Language Understanding (NLU)?

Natural language understanding is a smaller part of natural language processing. Once the language has been broken down, it’s time for the program to understand, find meaning, and even perform sentiment analysis.

The program breaks language down into digestible bits that are easier to understand. It does that by analyzing the text semantically and syntactically. 

Semantically, it looks for the true meaning behind the words by comparing them to similar examples. At the same time, it breaks down text into parts of speech, sentence structure, and morphemes (the smallest understandable part of a word).

Unlike structured data, human language is messy and ambiguous. As a species, we are rarely straightforward with our communication. Grammar and the literal meaning of words pretty much go out the window whenever we speak.

In fact, “out the window” is a great example. I, of course, didn’t mean that I throw things out a literal window, especially since I was talking about intangible concepts rather than solid objects. 

And so it is when you ask your smart device something like “What’s I-93 like right now?”.

If you were being literal, you might get an answer like, “It’s long, gray, and has cars driving on it. It was recently paved between exits 36 and 42.” But you probably wanted to know what the traffic conditions are.

That’s where natural language understanding comes in. It’s taking the slangy, figurative way we talk every day and understanding what we truly mean.

What Is Natural Language Generation (NLG)?

Once a chatbot, smart device, or search function understands the language it’s “hearing,” it has to talk back to you in a way that you, in turn, will understand.

That’s where NLG comes in. It takes data from a search result, for example, and turns it into understandable language. So whenever you ask your smart device, “What’s it like on I-93 right now?” it can answer almost exactly as another human would. 

It may say something like, “There is an accident at exit 36 that has created a 15-minute delay,” or “The road is clear.”

NLG is used in chatbot technology, as well. In fact, chatbots have become so advanced; you may not even know you’re talking to a machine. 

Using NLP, NLG, and machine learning in chatbots frees up resources and allows companies to offer 24/7 customer service without having to staff a large department.

NLG can be used to create data-based content at scale, as well. If you produce templated content regularly, say a story based on the Labor Department’s quarterly jobs report, you can use NLG to analyze the data and write a basic narrative based on the numbers. 

You and your editorial team can then concentrate on other, more complex content.

The Associated Press already uses NLG to create earnings reports, according to the Marketing Artificial Intelligence Institute

What Is the Difference Between NLP, NLU, and NLG?

NLP, NLU, and NLG all play a part in teaching machines to think more like humans. They simply tackle different parts of the conversational AI problem. How do you get machines to recognize, understand, and generate natural language? How do you use it to answer search queries and create content at scale?

Let’s take a specific example to illustrate just how these functions work together.

You get home from work and wonder how your stocks did today. After you tell your smart device to turn on your lights and crank up the heat, you ask, “Hey Google, how did the stock market do today?”

Your Google Home device listens to your query, and then NLP kicks in. It takes your question and breaks it down into understandable pieces – “stock market” and “today” being keywords on which it focuses.

Then it compares your query to similar queries made to Google in general and tries to understand what you’re asking. That’s NLU.

Once it understands that you want to know the closing numbers for NASDAQ, Dow Jones and the S&P 500, it crawls the web for content that best answers your question. 

Once it has data from a reliable source, like Bloomberg, it pulls the relevant data and delivers it in plain language. That’s NLG. Its answer is something like, “According to Bloomberg, the NASDAQ was down 1.5 points, but the Dow was up 77, and the S&P was up 5 points.”

You may then ask about specific stocks you own, and the process starts all over again.

The great thing about machine learning is that, if you ask those two questions regularly, your smart device may start anticipating your request, giving you your stock numbers along with the overall market numbers at the same time.

Why Is All of This Important?

The Marketing Artificial Intelligence Institute underlines how important all of this tech is to the future of content marketing. One of the toughest challenges for marketers, one that we address in several posts, is the ability to create content at scale.

Now, imagine you’re on the other side of that search request. You’re the one creating content for Bloomberg, or CNN Money, or even a brokerage firm. You’ve done your content marketing research and determined that daily reports on the stock market’s performance could increase traffic to your site.

But creating the same report every day is time-consuming. NLP, NLU, and NLG can create content for you. NLP and NLU will analyze content on the stock market and break it down, while NLG will take the applicable data and turn it into a templated story for your site.

But there’s another way AI and all these processes can help you scale content. 

Imagine you had a tool that could read and interpret content, find its strengths and its flaws, and then write blog posts that meet the needs of both search engines and your users. 

We’re actually getting closer and closer to that tool.

In fact, MarketMuse’s tool comes pretty close. It will use NLP and NLU to analyze your content at the individual or holistic level. While it can’t write entire blog posts for you, it can generate briefs that cover all the questions that should be answered, the keywords that should appear, and the internal and external links that should be included.

AI and machine learning have opened up a world of possibilities for marketing, sales, and customer service teams. Some content creators are wary of a technology that replaces human writers and editors.

But it can actually free up editorial professionals by taking on the rote tasks of content creation and allowing them to create the valuable, in-depth content for which your visitors are searching.

What you should do now

When you’re ready… here are 3 ways we can help you publish better content, faster:

  1. Book time with MarketMuse Schedule a live demo with one of our strategists to see how MarketMuse can help your team reach their content goals.
  2. If you’d like to learn how to create better content faster, visit our blog. It’s full of resources to help scale content.
  3. If you know another marketer who’d enjoy reading this page, share it with them via email, LinkedIn, Twitter, or Facebook.

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.