What is natural language understanding?

How we use artificial intelligence (AI) in our day to day lives is increasing at pace.

Think about some of the ways in which you go about acquiring information every day. Things like using a search engine or asking a digital assistant about the weather or the traffic on your route to work all rely on AI. More specifically, they use natural language understanding (NLU) to understand better exactly what it is you are asking. Such technology ensures Google, Alexa, or Siri can give you a relevant, contextual response.

From a business perspective, harnessing the power of NLU has enormous potential. Using NLU enables you to serve your client base better. It may also save you a significant amount of time and money, allowing you to redirect your resources elsewhere. All these benefits can unlock considerable growth potential for your business.

Here is a look at how natural language understanding works and some examples of how you might use it in your business.

How does natural language understanding work?

Global research and advisory giant Gartner defines NLU as:

“the comprehension by computers of the structure and meaning of human language (e.g., English, Spanish, Japanese), allowing users to interact with the computer using natural sentences.”

IBM, which offers a specific Watson NLU tool gives a more technical summary, saying its tool can:

“Analyze text to extract metadata from content such as concepts, entities, keywords, categories, sentiment, emotion, relations, and semantic roles using natural language understanding.”

In simple terms, NLU uses standard language conventions, such as grammar rules and syntax, to understand the context and meaning of speech or written text. NLU seeks understanding beyond literal definitions of language, to interpret, understand, and react to communication the same way we would as people.

Businesses, and specifically, SaaS tools, rely on NLU for a variety of general tasks, including:

• Language detection. NLU enables software to understand the language of a piece of writing automatically.

• Sentiment analysis. NLU interprets the use of words and their intent within spoken or written text and categorises the sentiment accordingly. For example, NLU may apply general classifications, such as positive, neutral, or negative, to sentiments. Alternatively, NLU systems may go into greater detail and be more specific around the emotion a text is conveying, using classifications like angry or confident.

• Topic classification. NLU interprets language to automatically sort queries into specific, pre-defined topics, from where it is easier to deliver a favourable outcome to the user.
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We will look at specific, real-world use cases of these tasks later.

What is the difference between natural language understanding (NLU) and natural language processing (NLP)?

The terms natural language understanding (NLU) and natural language processing (NLP) are often used interchangeably. However, such use of these terms misinterprets what each means, leading to misunderstanding and confusion about what specific types of technology can achieve.

NLU is a sub-field of NLP. What is the difference between the terms?

As we highlighted above, the purpose of NLU is to interpret human communication in context.

In contrast, NLP is an umbrella term describing the entire process of systems taking unstructured data (a random collection of words) and turning it into structured data (contextually relevant sentences). On the other hand, NLU looks specifically at the rearranging of the data to analyse it in context and provide relevant outcomes to the user or business using it.

What can natural language understanding do? Three real-world use cases

We already touched on how businesses and software platforms can use NLU for tasks like language detection, sentiment analysis, and topic classification. Here are some real-world use cases where you might already use NLU individually and where it can potentially help your business.

1. Machine translation tasks

Have you ever used Google Translate? If so, you will have noticed the “Detect Language” feature. You can type in Danish, Polish, or whatever language you want, and Google Translate will automatically detect what you are typing. This is language detection in action!

Machine translation is excellent for translating documents like contracts, particularly if you work cross-border and don't share a common language. It is also a useful feature for customer service platforms. Helpdesk software, for example, will often include language detection software. Such a feature can:

• Enable your business to answer customer queries not in your native language.
• Direct customer service queries are directed to the correct location if you have offices in multiple countries.
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2. Contract automation and analysis

NLU can play a crucial role in both the automation of contract creation as well as the analysis of contracts. Legal software with analysis functions relies heavily on both sentiment analysis and topic classification while using NLU in general to understand the context of what is written in a legal context.

You can then identify sections of contracts that you may need to query or where you have written a clause that might be unenforceable, for example.

While lawyers widely use such tools, non-legal businesses can reduce costs by using software for contract creation and analysis rather than consulting with legal experts. 

In fact, at Contractbook we recently mused upon the topic: <link to="" future="" institute=""> 

3. Customer service delivery and brand development

NLU has massive potential from a customer service and brand development perspective. In addition to using language detection and machine translation for overseas service delivery, NLU could help you achieve a variety of further outcomes, including:

• Customer feeling analysis. While tools like TrustPilot enable you to get ratings from your customers, rating platforms aren't the only place people talk about you. For example, you could use a data-gathering tool to consolidate all mentions of your brand online or across social media, and analyse the emotions driving them. If the general feeling about your brand isn’t what you want it to be, you can change it!

• Resolving customer queries quicker. By using topic classification, you can identify the relevant department within your business that needs to deal with specific customer queries and direct them accordingly. Rather than a customer service department bouncing queries around until they find the right department or individual, NLU will do the job for you. The outcome? Quicker query resolution and better customer outcomes overall.

• Using chatbot software. Having Live Chat functions on your website can have a substantial positive impact on conversions and customer sentiment. Thanks to NLU, you can use chatbot software tools to absorb most of the time needed, and you are also unlimited to how many customers you can chat with at a time. Use NLU to minimise the volume of Live Chat queries that require human intervention while watching your business soar!
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Using natural language understanding in your business

You are probably already using some NLU functions in your business without realising it. Even if you are not, you are almost guaranteed to be doing so in your day to day life.

Think about the parts of your business where you can improve operations, processes, and outcomes. From your ability to deal with multilingual queries and the speed of your customer service response to ensuring your contracts are 100% correct, NLU has the potential to transform your business and drive growth across both revenue and profitability.