Machines need a deep understanding of natural language for businesses to transform data into knowledge and insight. Natural language processing (NLP) and natural language understanding (NLU) make it possible for computers to understand language, whether typed or spoken. NLP breaks down and processes language, while NLU provides language comprehension.
NLP and NLU work together, yet they each perform different functions. They also both have distinct roles in how they help machines learn and process language. So while many still use NLU and NLP interchangeably, there’s plenty more to the story.
NLU vs. NLP
Natural language understanding is a subset of natural language processing (or superset in some cases). NLP “analyzes and processes” while NLU “learns and understands”. For machines to comprehend language, they must know the definitions of words and sentence structure, along with syntax, sentiment, and intent.
While NLP processes language, NLU acquires a deep understanding of language and meaning behind words. To better understand how NLU and NLP work in different ways, consider this:
- How does a voice assistant process a request to turn on a light? Natural language processing.
- How does a voice assistant recognize that a request was made? Natural language understanding.
Confused? Let’s break it down.
NLU Creates Order Out of Chaos
It can take a person 2,000 hours or more to learn a language. Grammar complexity and verb irregularity are just a few of the challenges. Language is data, but in its natural form, machines can’t understand it. Natural language understanding assigns structure, rules, and logic to language so machines know what we say.
NLU cracks the code of how language is arranged and transforms it into a machine-readable format. This is crucial because computers understand code, not natural language. They need data to interpret what they read and hear. NLU provides the data machines need to infer the meaning behind text.
Consider the way the human brain learns language in context. It decodes language via syntactic and semantic identification to classify words and intent. Like a human brain, NLU maps language input into data that NLP can use. It also uses disambiguation, a cornerstone of NLU, to analyze word context and remove ambiguity so meaning is clear.
To differentiate between natural language understanding and NLP, remember that:
- NLU is a subset of NLP (or a superset in certain scenarios).
- NLU learns language syntax, context, patterns, definitions, sentiment, and intent.
- NLU helps computers understand language.
NLP Turns Words into Action
NLP processes, analyzes, and reads large amounts of text and voice data so machines can respond to language-based commands. With NLP, humans can interact with machines, and machines can interpret and manipulate that language.
This can be broken down into five steps:
- Lexical analysis breaks language down into paragraphs, sentences, and words.
- Parsing works out the grammatical structure of sentences.
- Semantic analysis applies dictionary meanings of words.
- Discourse integration makes sense of language meaning with context.
- Pragmatic analysis extracts information from text.
Examples of NLP include voice search, email filters, and smart assistants. It differs from natural language understanding in that:
- NLP is focused on the words that are said.
- NLP analyzes and processes language data.
- NLP uses AI to make sense of language.
The Success of NLU and NLP Depends on AI
Artificial intelligence is critical to a machine’s ability to learn and process natural language. It’s what generates the algorithms and rules for learning. So when building an NLP or NLP program, it’s important to choose the right AI approach.
The two most common approaches are machine learning and symbolic, each with pros and cons. Here’s how they differ:
Machine Learning (ML) AI: Data Training
Machine learning uses computational methods to train models on data and adjust (and ideally, improve)its methods as more data is processed. Rules are honed through repeated processing and learning.
The computational methods used in machine learning result in a lack of transparency into “what” and “how” the machines learn. This creates a black box where data goes in, decisions go out, and there’s limited visibility into how one impacts the other. This makes models highly susceptible to bias. What’s more, a great deal of computational power is needed to process the data, while large volumes of data are required to both train and maintain a model.
With AI driven by machine learning:
- Acquired knowledge trains machines autonomously.
- Computational resources are needed to process data.
- Large data sets are needed to train machines.
Symbolic AI: Embedded Rules
Symbolic AI uses human-readable symbols that represent real-world entities or concepts. Logic is applied in the form of an IF-THEN structure embedded into the system by humans, who create the rules. This hardcoding of rules can be used to manipulate the understanding of symbols.
Using symbolic AI, everything is visible, understandable, and explained within a transparent box that delivers complete insight into how the logic was derived. This transparency makes symbolic AI an appealing choice for those who want the flexibility to change the rules in their NLP or natural language understanding model.
Symbolic AI works differently than machine learning because:
- Humans write rules.
- Rules can be revised.
- Embedded knowledge trains machines.
Understanding AI methodology is essential to ensuring excellent outcomes in NLP and NLU. Hybrid natural language understanding platforms, like that offered by expert.ai, combine the best of machine learning and symbolic approaches to improve the accuracy, scalability, and performance of NLU/NLP technologies.