What Is Natural Language Generation?
Natural Language Generation is being used more and more to create exciting, meaningful content on a large scale. If you don’t understand what Natural Language Generation is, you may be missing out on this valuable technology. In this article, you will learn the answer to the question, “What is Natural Language Generation?”
Definition of Natural Language Generation (NLG)
Natural Language Generation, or NLG, is defined as the creation of content through the interpretation of data. This technology is able to process massive quantities of structured data in seconds and turn it into written narratives that are easily and universally understandable to human readers. Through data analysis, Natural Language Generation can produce financial reports, product descriptions, company profiles, marketing plans and much more in a very short time.
This content created by NLG processes is rich with information and specifically targets the right audience thanks to a complex set of algorithms that give context to the raw data. Natural Language Generation is a division of Artificial Intelligence (AI) programming and connects machines and humans through common communication. Closely related to Natural Language Generation, the processes NLP, or Natural Language Processing and NLU, or Natural Language Understanding, make up computational linguistics structures, giving life and meaning to vast sets of data.
Natural Language Generation: What It Can and Cannot Do
Natural Language Generation often focuses on building computer programs and content rich with data points and meaningful context. Sophisticated processing is capable of mining huge data sets, identifying patterns within this information to create information that is easily understood by humans, all in a fraction of the time it would take a human writer to generate. The process takes almost no time at all, so it is perfect for producing timely articles for immediate publication. Natural Language Generation processes write with great speed but they cannot read complex materials. For instance, the process of interpreting a book into structured data is a job for Natural Language Understanding, not Natural Language Generation.
How Does Natural Language Generation Work?
Simple NLG Tools
Simple, basic Natural Language Generation software is typically capable of taking prewritten sentences and filling in the blanks. For example, “The travel time from <<location>> to <<location>> via <<route>> is currently <<time>> minutes.” The system takes four pieces of data as input and creates meaningful content using human-like language. A basic content generator such as this example turns data points into a sentence that is now meaningful to humans. Though simple, these types of systems work for many applications, with obvious limitations with regard to maintenance and scale. Building content generation systems like these can be simple, but NLG is capable of much more powerful content creation.
Advanced NLG Tools
Complex Natural Language Generation, on the other hand, requires technology that far exceeds the fill-in-the-blanks example of more simple applications. More advanced systems are focused on large amounts of data and the creation of human-readable conversion of this information into articles, texts and other applications. The aim for these applications is to increase marketing performance through higher productivity that delivers more personalized, complex content delivery.
One example of complex Natural Language Generation is the ability to create an organized, information rich article from data such as customer preferences and patterns. The machine is able to scrutinize what customers want, identify the company’s offering and create content that will effectively drive the sales cycle, all from existing company data. Applying this ability helps reach the audience quicker with relevant content, a huge advantage to brand awareness and direct interaction with the consumer base. The content will continue to build by creating groups of ideas that become more and more relevant to the audience and more effective as time goes on.
How Does Natural Language Generation Work?
Whether simple or complex, there are basic stages to the Natural Language Generation process that are common no matter what the desired application. These stages are:
- Decide – select what information will be mentioned in the content.
- Structure – decide how the data will be organized including what points to mention first, last, etc.
- Collect – improve the content readability by merging sentences that sound more natural as one.
- Wording – choose descriptive but easily understandable words, for example “travel time” rather than “navigation allotment”.
- Expression – choose identifying descriptions for regions and objects; for example, “Midwest” to refer to a certain region of the United States.
- Creation – create the actual text that follows correct syntax and grammar rules.
- Evaluation – through testing, Natural Language Generation research professionals determine how well the system is working. Through human assessment as well as comparison metrics, NLG systems are able to learn and become better and better.
How is Natural Language Generation Used?
Media outlets already use Natural Language Generation to give complicated sets of data context for human consumption. You probably didn’t realize that you’ve been reading stories, charts and articles automatically created for quite a few years. Instead of relying on human writers or reporters to gather and write the content, news stories are automatically written by computers. Common topics include weather, traffic, sports recaps and scoreboards, financial reports, real estate data and financial earnings. The Associated Press has used automated content to cover minor league baseball games, which consist of over 10,000 games per year and so much more. Most companies understand that there is value in their business intelligence (BI) that is broader than a few points in a sentence. Often this requires complicated business analytics, gathered and interpreted by a human staff. These analytics eventually give insights into customer patterns, opportunities and brand weaknesses and challenges.
NLG is Faster…and More Effective
The process of analyzing data is slow, expensive, and can be inconsistent. Often analytics are done manually by a team of experts over weeks or even months.
Now, with Natural Language Generation, the bottleneck of having a team of analysts review the data and explain it in simple terms can be faster and more efficient. Natural Language Generation use in B2C applications is making headlines. With AI marketing, companies can offer personalized recommendations directly to consumers through machine-generated content. These B2C applications create a content ecosystem that references millions of products and recommends them to consumers based on business intelligence data driven decision making. Possibly the most popular use of this technology is with home assistant devices such as Alexa and others.
B2B focused content from NLG systems deliver effective, intelligent text on a grander scale than most companies could afford on their own. For a business looking to increase their brand awareness, automatically generated content is an invaluable tool for creation of various kinds of texts. In one example, AI systems are put to use analyzing vast sets of data about consumers. Automated content is used to generate thousands of pieces of content based on this data.
This content has the optimal wording, vocabulary and context that engage and inspire the customer and positively affect the company’s bottom line. In comparison, most companies rely on human interpretation making educated guesses on what B2B content will be effective for their purposes. There are simply too many variables on context to make human content generation effective and meaningful. Focus groups, testing and team decision making all have different variables making them inefficient and less timely than use of Natural Language Generation processes.
Content is king
When it comes to content for enterprise companies, AI-based content writing can provide effective content in a fraction of the time with very little human input. By analyzing huge sets of company data, Natural Language Generation can create compelling content relevant to the reader. For instance, NLG can create entire articles that will create interest and enhance SEO for the company’s website. All this new attention will amplify the sales funnel and create new customer relationships while strengthening the current market base.
Building natural language generation systems on your own is not an easy task. Even though NLG technology is used more in the last years, creating high-quality conversational content based on data still requires a high-level of expertise. Phrasetech is the leading NLG solution for enterprise companies in the online world. With the ability to create high quality human-like content on a massive scale, Phrasetech’s innovative technology is designed to deal with the challenges of online content creation.