The world of Artificial Intelligence (AI) includes two closely related, yet very different disciplines: Natural Language Understanding (NLU) and Natural Language Generation (NLG). While both technologies process language data, they have different goals and methods.
NLU is concerned with the ability of computers to understand, interpret, and process natural language. It is about analyzing human language to capture the semantics, or meaning,of text. Once the meaning is determined, software can use it as the basis for performing actions,providing answers, and carrying out other functions.
NLG is the name given to the ability of computer systems to generate text. This can take various forms, such as “human” responses in chatbots, full-length articles, and even poems.
This article will look at the differences between NLU and NLG, which are two components of Natural Language Processing. We will give an overview of the many ways these forms of AI can be applied and how rellify uses them.
What is the difference between NLU and NLG?
Natural Language Understanding (NLU) uses machine learning-based artificial intelligence and so-called large language models to interpret and “understand” human language. Roughly speaking,software analyzes huge amounts of text to find recurring patterns. Complex statistical algorithms then look at new text and assigns “presumed” meanings to it. If the language models are large enough and the statistical algorithms are suitably precise,the semantic meaning of natural language texts can be acccurately determined. This enables machine processing and application of information.
NLU is a complex process that involves, for example, the identification of words and phrases,the syntactic analysis of sentences, and the determination of the meaning and intention of a sentence. Various techniques and algorithms are used, such as machine learning, deep learning, and neural networks, to identify the meanings of and relationships between words and sentences.
You can find NLU being used in voice assistants, chatbots, translation tools, sentiment analysis, speech recognition, and many other places. As NLU technologies and algorithms continue to evolve, computers are becoming better and more accurate at understanding and interpreting human speech, leading to better user experiences and more effective applications.
NLG, on the other hand, refers to the ability of computers to analyze structured data andgenerate human-readable language. In some ways, it is the inverse of NLU. In Natural Language Generation, software assembles text that is statistically plausible based on learned patterns andprobabilities. This allows computers to output information in quasi-natural language to produce reports, formulations, descriptions, summaries, and other material.
The original structured data can come from a variety of sources, such as databases, sensors, and other machine data. NLG can be combined with other technologies, such as NLU, to enable full human-computer linguistic interaction.
NLG is used for automating report generation, summarizing data, creating product descriptions, generating text for social media, and many other uses. With advances in artificial intelligence and machine learning, NLG is becoming more powerful and accurate. It has the potential to be used more widely in many fields to generate text with improved efficiently and accuracy.
What is the focus of rellify?
rellify is the leading provider of content intelligence. And content intelligence is the key to good, relevant content. Content that isn’t relevant doesn’t get noticed, so content creators must identify relevant topics. They need to understand which topics, keywords and questions must be addressed to create relevant content on those topics. However, given the gigantic amounts of content on the internet, thorough analysis can no longer be done without machine learning.
This is where rellify Content Intelligence comes in. Using NLU and Deep Learning, we crawl hundreds of thousands of sources on the Internet for our customers on a specific topic. The result is a NeuraVerse. This specific slice of the internet contains all publicly available content, conversations and media around your business, market and competitors. In addition, your entire company knowledge can also be included in this analysis.
Content Intelligence thus enables content producers to create tailored briefs for individual articles with just a few clicks.
How does rellify use NLU?
The rellify Content Intelligence platform uses both NLU and NLG. However, the focus is on NLU. With our Deep Learning technology, we use Natural Language Understanding to better understand the web and its content.
For example, rellify can use NLU to identify, understand, and index millions of online sources on a given topic in a very short time. From these insights,rellify can infer topics that are of particular relevance. Then, using its machine learning algorithms,the AI clusters the keywords relevant to those topics. This is how Content Intelligence is created.
Content Intelligence is the most important resource in the daily battle for attention online. You can create content with ChatGPT — but is it accurate? And does it meet your brand, tone, and style requirements — your specific needs? And most importantly:Does content written by ChatGPT & Co. hold anyone’s interest. In short, is AI-generated content relevant enough to attract attention and readers?
No, it is not. Content Intelligence enables you to develop editorial plans that can generate long-term,sustainable relevance, attention and topic leadership. But you still need the “person in the loop” — the experienced writer — to produce great content. At the very least,you need that experienced wordsmith to review and polish any machine-generated content. Because even the best AI can’t write in your style and take into account all your brand specifics.
Does rellify also offer NLG?
Of course, rellify offers more than just Content Intelligence based on NLU. The rellify platform offers Natural Language Generation (NLG) to content marketing teams for their writing processes to help create good content faster.
In rellify’s „Write“ function, all you have to do is click the “Magic Button” during the writing process. Depending on where you are in the writing process,you can choose from several options for using the NLG (currently GPT-3.5), including:
- Create an outline for an article
- Suggest a headline
- Answer a question
- Elaborate on a topic
- Write a summary
There are many possibilities. For example, you can use our NLG tools to create a framework for the article and even write the article. However, you can have our experienced editors fact check and rewrite the text as needed, or take care of that with your own team. Because we can generate a NeuraVerse that is specific to your business,rellify’s AI-generated content is superior to what you would get by using only ChatGPT. But whether you go straight AI or add experienced writers and editors into the mix, you can have relevant, brand-appropriate content in no time!
Conclusion: NLU and NLG – Accelerate Your Content Creation
rellify Content Intelligence leverages both NLU and NLG in its platform. Combined with Deep Learning, NLU helps to identify, analyze and understand hundreds of thousands of sources onthe internet on a given topic. From this, well-tailored briefs can be created for articles.
In addition, rellify also offers the possibility to use Natural Language Generation in the writing process to quickly create good content. Here, leading language models such as GPT-3 from OpenAI, create text modules from which your authors can create higher quality content in less time.
Do you want to offer more relevant content with rellify™ Content Intelligence?
Then talk to our consultants:
Author: Sebastian Paulke
… has published over 1,200 technical papers, as well as many studies and research on innovation technologies over the past 20 years. He is a research professor of AI at the International Innovation Center of Hankou University, in Wuhan, China.
At rellify, Sebastian Paulke is responsible for corporate communications as well as content strategies.
