What is the difference between NLP, NLU, and NLG?

Key takeaways:

• NLP includes NLU (understanding) and NLG (generation).

• NLP processes language, NLU extracts meaning, and NLG generates human-like text.

• NLP uses raw text, NLU uses unstructured language, and NLG uses structured data as output.

• NLP provides insights, NLU provides context and intent, and NLG generates understandable text.

Nowadays, the way we interact with technology often mimics human conversation. It is essential to understand how computers process and generate language, whether we are creating content, translating, or interacting with virtual assistants. This leads us to three key ideas: NLP (natural language processing), NLU (natural language understanding), and NLG (natural language generation). Each plays a unique role in how machines process language.

In this Answer, we’ll explore these terms in detail, using examples, and discuss why it’s crucial to understand their differences.

We can see that NLP is the bigger basket; inside it, there is a subset NLU and another subset NLG. NLP is nothing but the combination of NLU and NLG. They are connected, but they are distinct.

• NLP recognizes and processes important data, organizing it into text, numbers, or computer language.

• NLU comprehends human language and transforms it into data.

• NLG takes structured data and produces meaningful narratives.

NLP

This is where computers read languages. They analyze the text and convert it into structured data. It’s a component of artificial intelligence that handles a ton of human language. It’s like a complete process where the system communicates with people. The system takes care of everything from comprehending information to making decisions during conversations. This includes activities such as reading, understanding, and determining how to reply.

NLU

It’s a subset of NLP, focusing on the reading part. Tasks like spotting profanity, analyzing sentiment, and categorizing topics fall into this category. Its goal is to help machines understand information by deciphering the meaning of the data. This includes considering the context, expression, and intent behind the text. Different methods and rules are applied to make sense of the information and uncover the purpose or message conveyed in the text.

NLG

This is smart. Computers compose meaningful text here; yes, they generate it. It’s the conversion of structured data into text. NLG is a method for creating sentences that are clear in everyday language. It transforms organized information into something easily understandable, almost like reading thousands of pages in just a second.

Conclusion

In the world of smart computers, NLP, NLU, and NLG are like big helpers. They make machines talk to people cleverly. NLP is the main framework, and NLU understands human language, while NLG creates text that sounds human. As tech improves, these three helpers working together will be crucial for how people and machines talk to each other.