If you’re like most of us, Natural Language Generation is part of your daily life and probably part of your business as well.
It’s everywhere: in the results of your online searches, in voice assistants like Amazon’s Alexa and Apple’s Siri and in chatbots that offer a personal assistant to help answer questions.
NLG is one of the many forms of Artificial Intelligence (AI). And it’s a strong ally for businesses that need to respond to a variety of customers, all at once, with personalized information.
What is Natural Language Generation?
Natural Language Generation comes under the umbrella of Natural Language Processing. And NLP is one of the six main branches of Artificial Intelligence. (Experts organize the elements of AI in different ways. This is one commonly accepted method.)
- Machine Learning, which allows computers to process feedback and learn without new programming.
- Neural networks, the use of algorithms to mimic the human brain’s affinity for analyzing and understanding analyze data.
- Robotics, which combines software, mechanical engineering, electrical engineering and other sciences.
- Expert systems, which are computer systems that tap data bases and analytics to make decisions.
- Fuzzy logic, a method of adding flexibility to reasoning when a problem contains uncertainties.
- Natural Language Processing, programming that can process human language. This branch includes Natural Language Understanding (NLU) and Natural Language Generation (NLG).
I hear you asking: How might I best make use of these new tools?
Well, let’s define them, first, with a brief review.
What are the differences between NLP, NLU, and NLG?
Natural Language Processing (NLP) tries to understand natural language by analyzing the meanings of words, the structure of sentences and other clues.
One part of NLP is Natural Language Understanding (NLU), which uses deep learning to process and comprehend text and its meanings, emotions, syntax and relationships.
When someone types words into a Google search, the search engine uses NLU to determine what people are searching for.
The other side of NLP is Natural Language Generation (NLG). It transforms data into understandable language, writing sentences, paragraphs and even complete articles that seem natural to human readers.
NLG uses algorithms to solve the extremely difficult problem of turning data into understandable writing. The programs use deep learning, machine learning, neural networks and other algorithms to achieve tasks like understanding what the next word in a sentence should be, based on the words that came before it and the data still left to process.
While NLU’s focus is on your computer’s ability to read and to listen, NLG allows it to write and to speak.
Natural Language Understanding and Natural Language Generation are two subsets of Natural Language Processing.
What are some examples of Natural Language Generation?
When Siri, Alexa, or Cortana answer your questions, they’re using Natural Language Generation and other programming to translate text into a spoken form.
When “Sam, your personal assistant” responds to your written query (perhaps as you’re waiting for a live person to respond), he is using chatbot technology to interpret what you have typed and then to respond with an appropriate message through NLG technology. The software searches for keywords in your questions, and then uses specific applications to generate pre-written answers based on the frequency of their usage.
That’s a lot to consider, sure, but there’s an easy way to understand the distinctions between these various forms of AI. It’s so much more than Robotic Processing Automation, a form of business process automation technology used to do repetitive, low-value work.
Thanks to the data scientists who’ve done all the research and much of the work for us, NLG is a boon to marketers hoping to personalize responses using natural language to clients.
The most sophisticated NLG model, GPT-3, or Generative Pre-trained Transformer 3, can write poetry, prose and even computer coding that is hard to distinguish from that created by humans. OpenAI, a San Francisco-based research lab, created GPT-3. It has 10 times the capacity of the second-best model, Microsoft’s Turing NLG.
How are businesses using NLP, NLU and NLG?
Well, we are likely to see more and more Natural Language Processing as companies adopt both written and spoken Intelligent Personal Assistants (IPAs) to answer their customers’ questions. Businesses in many sectors benefit from the sophistication that NLP allows them to offer clients and customers:
- Digital Commerce: Digital marketers create custom content, seasonalize or push specific promotions, and personalize offerings to individual shoppers.
- Human Resources: HR departments automate the writing of job postings and their publication in a variety of websites.
- Media: For publishers and the media, NLG solutions allow for automated content generation. The New York Times, Forbes, the Los Angeles Times and many other media outlets use NLG.
- Resorts, Hotels, Entertainment: Venues and promoters take advantage of NLG. They automate descriptions of rooms, grounds and offerings – and can update them quickly for foul weather or other emergencies.
- Brick-and-Mortar Stores: Retail chains customize landing pages for individual stores with the local hours of operation, addresses, directions, and other information. Chains easily mount national promotions, even on hundreds of stores’ individual pages, using NLP tools.
- Insurance: Companies customize offerings for particular searches, based on location and demographics. They also promote the appropriate form of insurance — rental to renters, home and auto to homeowners and drivers.
- Real Estate: Firms generate application-specific local reports based on the availability of homes and the demand for mortgage information. The firms also can use NLG to create press releases and promotions.
What can Natural Language Generation and deep learning do for your business?
We’ve looked at some specific business items, but there are a variety of general benefits:
- If you are the content author, you are likely to have to spend less time on routine and repetitive tasks.
- You can use NLG systems to optimize your output, even as you improve customer experience, engagement and customer satisfaction.
- It can even help in minimizing hiring costs, since fewer copywriters are needed to create narratives. If you are marketing in more than one language, your work can be translated simultaneously.
All of this provides a competitive edge to your business. NLG can make you more efficient by reducing costs through automation. It frees up people for bigger jobs and gets data to key people in a way that’s easy to comprehend. That helps them make better decisions.
You’ll deliver a stronger and more personalized digital experience to your customers. In web terminology, this leads to better visibility across search engines and to the strengthening of your SEO strategy.
Savvy use of NLG can bring more visitors to your site and keep them there longer. Your business will be seen in the best light, because NLG automatically uses proper grammar, sentence structure, syntax, and spelling.
You can train it to support your specific brand, generating quality content that matches your expected cadence, tone, and voice. This helps to project your business’ image with a strong, consistent message.
Put AI to work for you
Contact rellify, the content performance platform, to find out how it uses Intelligence Augmentation, machine learning and NLP to create content that naturally ranks high in search engine results.
