What is Natural Language Understanding?
Natural language understanding refers to a computer’s ability to make sense of a human’s speech. Put simply, it’s the computer’s way of understanding data before it is able to process it.
Speech is unstructured from a computer’s point of view. With natural language understanding, a computer can interpret the data, structure it, and the process it to respond.
NLP vs. NLU: Breaking Down the Main Difference
When you first learn about how speech-enabled tools work, you’ll come across the term natural language processing (NLP), which is focused on how a computer works to process language and communicate from human to system.
Natural language understanding, a subset of NLP, is all about how the computer itself works to understand a human’s language by structuring the otherwise unstructured data from speech.
How Does Natural Language Understanding (NLU) Work?
NLU works through a series of steps that transform the input of a sentence or paragraph into an output.
Here’s how the flow of information goes:
1. Tokenization
The first step breaks down the sentence or paragraphs into individual words, also known as tokens. A token can be symbols, punctuation, or words.
2. Lexical Analysis
Next up, the tokens are categorized into what form of speech they are, as in nouns, verbs, adjectives, etc.
3. Syntactic Analysis
Finally, the tokens are then analyzed for their grammar, in order to parse what the role of the word should be. At this stage, the computer can also discern whether the word may be interpreted in different ways (context is pivotal to better understand).
You can think of this much like how a child learns to read and understand for meaning. Like a human brain, you can expect to break down each word, then piece it together and use the clues of grammar and context to make sense of the sentence as a whole.
What are NLU Techniques?
NLU works by way of semantic analysis, which is the application of a computer’s algorithm applied to the text, in order to decipher meaning from its context. This works in opposition to a rules-based approach, which would leave out the role of context.
NLU is focused on deriving meaning, even if the words that are strung together aren’t grammatically correct.
This is part of why aiOla’s speech-enabled AI technology can work across industry settings to complete mission-critical tasks. For example, users can speak into any enabled device to run through an important and time-sensitive checklist. Even if they don’t speak in full sentences or only use business-specific jargon, aiOla’s proprietary technology is capable of discerning meaning and only focusing on the words that are relevant to the task at hand.
Common Applications of NLU
Whether you’re looking to use NLU for your own business or not, the chances are likely that you’ve come across it in applications already. Want a natural language understanding example?
Here are a few:
Chatbots and Virtual Assistants
Alexa, Google Assistant, and Siri are all powered by NLU. These AI-based assistants can execute common tasks that would otherwise require human intervention, such as: answering questions, setting alarms, reading texts, and more.
Sentiment Analysis
NLU is what drives sentiment analysis, or a computer’s ability to assess a human’s emotional tone through data. This is used often in marketing, sales, customer service, and product development to better understand customer feedback and gauge public opinion, for example.
Data/Information Extraction
Natural language understanding is needed to pull information from unstructured text data, including: research papers, news articles, and legal documents. This can be helpful when trying to summarize content and pull out just the key facts.
What are Common Challenges with NLU Technologies?
As with anything technology-driven, there are challenges that must be faced and overcome through trial, error, and constant innovation. When it comes to natural language understanding in AI, these are some recurring hurdles:
Addressing Ambiguity and Complexities
Whether it’s a computer or a person trying to pull meaning from conversation, language can be ambiguous. There are many words that mean different things, so context is key to understanding properly. To exemplify, let’s take the word, “facts.” A person could be saying “fax” or “facts,” it really depends on the rest of the sentence to know its use.
Multilingual and Cross-Cultural Challenges
A common challenge for many AI-powered speech recognition tools is languages and dialects. Language understanding is a key to ensuring NLU is accurate. Did you know that aiOla is a first-of-its-kind technology that can actually understand hundreds of languages in any accent, along with business-specific terms. This means that businesses can empower their employees with aiOla to get more done using nothing but their words, and there’s no need to be concerned over its ability to accurately understand your team.
Privacy Concerns
Another common question that comes up is ethics and privacy in terms of collecting and analyzing data from spoken words. When you use a speech-powered tool, it’s vital to understand the privacy terms and conditions.
What are Real-World Use Cases of NLU?
Artificial intelligence and its ability to understand human speech is changing the landscape for how businesses achieve their goals and how people get things done. You can see examples of NLU in action across virtually every industry, including:
Customer Service Automation
Machines can pick up on what a customer needs and route customer support tickets to the right department to be addressed for resolution. This helps to expedite issue resolution times, resulting in increased customer satisfaction.
AI-Driven Content Creation
Thanks to NLU, tools can create visual or written content from text prompts. From social media posts to product descriptions, AI is helping businesses to generate and create content faster.
NLU Applications in Healthcare
The filled of healthcare is filled with the need to input data accurately for safe recordkeeping and reference. NLU systems can be deployed for conversational documentation for clinical review or for computer-assisted coding (CAC) for billing, for example. In aiOla’s case, pharmaceutical companies can rely on aiOla to execute facility inspections, run through safety protocol checklists, or perform pharmaceutical auditing through speech, to name a few use cases.
To learn more about how you can use aiOla in action, check out these industry overviews.
Closing Thoughts
Without getting too technical or complex, you can see that the use cases of natural language understanding are ubiquitous. An easy way to remember what the term means is to focus on the word “understanding,” which describes the process of how a computer deciphers meaning from human speech, mostly with the aid of context.
NLU is transformative because it bridges the gap between humans and computers, making it possible for computers to communicate and respond to humans’ needs.
