Demystifying AI Part 1: NLP vs. NLU
Conversational chatbots of the past have been confined to rule-based decision trees that stick to a scripted approach. While they do handle some requests well, shouldn’t your chatbot do more? Demystify AI by learning the details of NLP vs. NLU
Recent advances in Conversational AI have led to new solutions that use Natural Language Processing (NLP) and Natural Language Understanding (NLU). In this post, we’ll dive into the technology powering these recent advances and how support desks can take advantage as they scale. Specifically, we’ll cover the following:
- What is Natural Language Processing (NLP)?
- What is Natural Language Understanding (NLU)?
- NLP vs. NLU – What’s the difference?
Natural Language Processing
NLP is a branch of AI that uses computers to process and analyze large volumes of natural language data. Given the complexity and variation present in natural language, NLP is often split into smaller, frequently-used processes. Common tasks in NLP include part-of-speech tagging, speech recognition, and word embeddings. Together, these help AI converge to the end goal of developing an accurate understanding of natural language structure.
Natural Language Understanding
NLU is also a branch of AI and is actually a subset of NLP. It focuses on extracting meaning from human language. Let’s take an example from a typical IT Support Desk. Whereas one employee might say “I need a new laptop,” 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 lead to the ordering of a new laptop from the company’s service catalog, but NLU is what allows AI to precisely define the intent of a given user 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.
NLP vs NLU
So, what’s the difference between NLP and NLU? In essence, NLP focuses on the words that were said, while NLU focuses on what those words actually signify. Some users may complain about symptoms, others may write short phrases, and still, others may use incorrect grammar. Without NLU, there is no way AI can understand and internalize the near-infinite spectrum of utterances that the human language offers.
P.S. In our next post, we’ll take a deep dive into various ways Natural Language Understanding can help AI talk to your end-users like never before.