NLU vs NLP vs NLG: What’s the difference?

Natural language means the way we communicate with each other through spoken or written words as a humans. It’s a complex and ever-changing rules, syntax and semantics. Natural language is the foundation of understanding and generating human language through many applications in areas such as language translation, speech recognition, and text analysis.

Old chatbots were rule-based decision trees that followed a scripted approach. They do some things well, but shouldn’t your chatbot do more? Demystify AI by learning about NLP vs NLU.

Recent advancements in Conversational AI have brought new solutions that use Natural Language Processing (NLP) and Natural Language Understanding (NLU). Input data is key to machines to process and interpret user text or speech, to enable human to machine communication. This post will get into the tech behind these new advancements and how support desks can benefit as they scale. We’ll cover:

Machine translation is one of the methods in NLP to convert unstructured human language data into machine readable format, to analyze and process text data.

NLP stands for Natural Language Processing, and it’s a branch of artificial intelligence that uses computers to process and analyse large amounts of natural language data. Given the complexity and variability of natural language, NLP is often broken down into smaller, more common tasks.

Common NLP tasks include part-of-speech tagging, speech recognition, and word embeddings. Syntactic analysis and semantic analysis are key to understanding the structure and meaning of sentences. Syntactic analysis determines the grammar, semantic analysis determines the relationships between words and phrases, and overall context and user outcome. Together this helps AI get to the end goal of understanding the structure of natural language.

NLU is also a branch of AI and is actually a subset of NLP. It’s about extracting meaning from human language. Let’s take an example from a typical IT Support Desk. One person might say, “I need a new laptop,” and another might say “Yesterday my old laptop stopped working. I’ve tried all troubleshooting options, but I’ve had no luck. What should I do now?”

Both should order a new laptop from the company’s service catalog but NLU is what allows AI to precisely define the intent of a given use,r no matter how they say it.

As you can imagine, this requires a deep understanding of grammatical structures, language-specific semantics, dependency parsing, and other techniques. Word sense disambiguation plays a big role here by identifying the meaning of words based on the context surrounding them, so the intended message is understood correctly.

Another technique in NLU is named entity recognition which is about identifying and classifying key elements in text to turn unstructured data into structured data. This is important for many NLP tasks such as language translation and question answering.

Natural Language Generation (NLG) is a subfield of Artificial Intelligence (AI) that turns structured data into human-like text. It allows machines to produce narratives, reports, summaries or conversational responses. While NLP and NLU focus on understanding and processing human language, NLG is responsible for generating natural language content from data inputs.
NLG is closely related to Generative AI, as both create human-like outputs. But while Generative AI is broader (text, images, audio, etc), NLG is focused on text. Language model like GPT-4 are used in NLG to produce coherent and contextually relevant text, so they are great for chatbots, automated reporting, and personalized content generation.

NLG systems follow these steps:

1. Data Analysis: The system analyses structured data, such as statistics or database entries.
2. Content Determination: The system decides what to say.
3. Document Structuring: It organizes the content logically and coherently.
4. Sentence Aggregation: The system combines related bits of information into one sentence.
5. Linguistic Realization: It converts structured data into natural sentences.
6. Surface Realization: The final step is grammatical refinement and stylistic tweaks.

Examples of NLG

• Automated Report Generation: Business reports, financial summaries, news articles.
• Conversational AI: Chatbots and virtual assistants.
• Personalized Content Creation: Product descriptions and marketing content for specific audiences.
• Data-Driven Storytelling: Narratives from large data sets, sports statistics or financial analysis.

So what’s the difference between NLP and NLU? In short, NLP looks at the words that were said, and NLU looks at what those words mean. Some users complain about symptoms, others write short phrases, and others use bad grammar. Without NLU, there is no way AI can understand and internalize the near infinite spectrum of utterances that human language offers. Understanding human language is key to understanding user intent.

NLG vs NLP vs NLU: What’s the difference?

• NLP (Natural Language Processing) deals with the processing and analysis of human language, enabling machines to understand, interpret, and generate natural language.
• NLU (Natural Language Understanding) focuses on extracting meaning and intent from text, allowing machines to comprehend human language in a more contextual way.
• NLG (Natural Language Generation) creates human-like text from structured data, enabling machines to produce natural, coherent, and contextually relevant responses.

NLG is used in many industries where data needs to be turned into understandable and engaging content. From automated customer support to dynamic content creation, NLG makes machines talk to users.

Machine learning is part of language systems and is used to train machines to learn from data and get better over time. In NLP, machine learning is used to train large language models with datasets of text or speech and to learn patterns and relationships in the data.

This allows machines to do sentiment analysis, entity recognition and language translation with high accuracy. Machine learning algorithms like RNNs and transformers are used in NLP and have achieved state of the art results in many tasks. Also machine learning is used in NLG to generate human like language and to make machines respond to user queries in a natural way.

NLP based and supervised chatbots have been around for a while, but with the advancements in NLU, we now have AI Assistants that can natively understand and converse with end users. Why can’t your chatbot do more? NLP and NLU convert unstructured data into actionable insights and various use cases.

With NLG, AI Virtual Assistants can do more than ever, generating natural and contextually relevant responses that feel human-like. This has led to technologies like Agentic AI and AI copilots, which use NLP, NLU and NLG to deliver more dynamic and autonomous interactions.

Aisera uses state-of-the-art unsupervised NLP and NLU to supercharge your enterprises with Agentic AI. Schedule a free AI demo to experience it, and we’d love to show you around.