While Natural Language Processing (NLP), Natural Language Generation (NLG), and Natural Language Understanding (NLU) are all critical pieces of AI, the differences are commonly confused. At a high-level, NLU and NLG are components of NLP. Let’s break down NLU vs NLP vs NLG and make sense of this AI alphabet soup.
Natural Language Processing (NLP)
NLP dates back to machine learning pioneer Alan Turing and his work, “Computing Machinery and Intelligence” where the question on whether or not machines can think like humans was proposed.
Today, NLP plays an essential part in how humans interact with technology, as well as in everyday life. NLP enables computers to understand the complexity of human language as it is spoken and written, using AI, linguistics, and deep machine learning to process and understand real-world input in an efficient manner. NLP has two subsections – NLG and NLU.
Natural Language Generation (NLG)
NLG software turns structured data into written reports, narratives, and explanations at the speed of thousands of pages per second. In short, NLG helps make data universally understandable and seeks to automate the writing of data-driven narratives including medical and financial reports, product descriptions, and more. Some relevant uses of NLG include:
- Analytics: Democratize data analytics and data-driven decision making by explaining insights that are easy to understand and produced at a high volume
- Advice: Automatically explain what courses of action to take in response to data, while complying to industry rules and regulations in every report
- Business Intelligence: Explain the results of data discovery or analysis in written languages
Ultimately, NLG is the next mile in automation due to its ability to model and scale human expertise at levels that have not been attained before. NLG can summarize greater amounts of data and explain analysis more in-depth. With that, Yseop’s NLG platform streamlines and simplifies a new standard of accuracy and consistency.
Natural Language Understanding (NLU)
While NLG software can write, it can’t read, which is where NLU comes in. NLU is a branch of AI that understands spoken or written language, with the goal to turn unstructured data (like text) into structured data. Uses of NLU include:
- Data discovery: Skim thousands of pages of written documents and capture data
- Smart searches: Find keywords in written text or speech, as well as key concepts
- Voice recognition: Speak directly to a computer or device and understand the spoken word through software, similar to Siri
NLU vs NLP vs NLG can be difficult to break down, but it’s important to know how they work together. Overall, NLP and other deep technologies are most valuable in highly regulated industries – such as pharmaceutical and financial services – that are in need of efficient and effective solutions to solve complex workflow issues.
Yseop’s vision is to empower companies and individuals to work smarter and faster by automating complex business processes through advanced NLG technology and a suite of specialist tools that support report writing, sales management and other business processes. These leverage artificial intelligence to make sense of complex data sets, generating written narratives accurately, quickly and at scale. To learn more about Yseop’s solutions and to better understand how this can translate to your business, please contact firstname.lastname@example.org.