Natural language understanding (NLU) has transformed the way companies interact with their customers. The ability to decipher customer intent from text messages, emails, and other forms of communication has become essential to businesses large and small.

What is natural language understanding (NLU)?

Natural Language Understanding (NLU) is a branch of artificial intelligence (AI). NLU is one of the main subfields of natural language processing (NLP), a field that applies computational linguistics in meaningful and exciting ways.

NLP is a broad term that encompasses several subfields such as information retrieval, information extraction, text mining, speech recognition, language models, dialog management, machine translation, conversational interfaces, natural language generation (NLG), and more. NLU is one of the most important areas of NLP as it makes it possible for machines to understand us.

The aim of NLU is to allow computer software to understand natural human language in verbal and written form. NLU works by using algorithms to convert human speech into a well-defined data model of semantic and pragmatic definitions.

There are two fundamental concepts in NLU:

Intent recognition

The aim of intent recognition is to identify the user's sentiment within a body of text and determine the objective of the communication at hand. Because it establishes the meaning of the text, intent recognition can be considered the most important part of NLU systems.

Entity recognition

The focus of entity recognition is to identify the entities in a message in order to extract the most important information about them. Entity recognition is based on two main types of entities, called numeric entities and named entities. A numeric entity can refer to any type of numerical value, including numbers, currencies, dates, and percentages. In contrast, named entities can be the names of people, companies, and locations.

For example, a request for a plane ticket to the Isle of Man on January 11 may be broken down in the following manner:

  • Plane ticket [intent]
  • Travel by plane [intent]
  • Isle of Man [location]
  • January 11 [date]

The purpose of NLU training data

Also referred to as "sample utterances", training data is a set of written examples of the type of communication a system leveraging NLU is expected to interact with. The aim of using NLU training data is to prepare an NLU system to handle real instances of human speech.

Training data organizes unstructured language into sets known as "buckets". The purpose of these buckets is to contain examples of speech that, although different, have the same or similar meaning. For instance, the same bucket may contain the phrases "book me a ride" and "Please, call a taxi to my location", as the intent of both phrases alludes to the same action.

How does NLU work in a chatbot?

Natural language understanding is used by chatbots to understand what people say when they talk using their own words. This allows for fluid conversations between humans and chatbots to happen. For an AI to be able to successfully deploy NLU, it must first be trained. By using training data, chatbots with machine learning capabilities can grasp how to derive context from unstructured language.

In the case of chatbots created to be virtual assistants to customers, the training data they receive will be relevant to their duties and they will fail to comprehend concepts related to other topics. Just like humans, if an AI hasn't been taught the right concepts then it will not have the information to handle complex duties.

If automatic speech recognition is integrated into the chatbot's infrastructure, then it will be able to convert speech to text for NLU analysis. This means that companies nowadays can create conversational assistants that understand what users are saying, can follow instructions, and even respond using generated speech.

To successfully implement NLU, a chatbot must be able to:

  • Understand and generate parts of speech
  • Extract and understand entities
  • Determine the meaning of words
  • Use other processing activities to connect together concepts, phrases, and grammar into a picture of intent and meaning

The difference between Bot and Conversational AI

An example of natural language understanding

A clear example of NLU at work can be found in your inbox. All major email solutions come with NLU-powered spam-filtering capabilities. These organize incoming emails to remove spam and computer viruses. Businesses can also use email filters to inspect outgoing emails to make sure all employees comply with company policy.

Natural language understanding applications and use cases

Conversational chatbots

Customer support has been revolutionized by the introduction of conversational AI. Thanks to the implementation of customer service chatbots, customers no longer have to suffer through long telephone hold times to receive assistance with products and services.

By implementing NLU, chatbots that would otherwise only be able to supply barebone replies can use keyword recognition to amplify their conversational capabilities. NLU-powered chatbots can provide instant, 24/7 customer support at every stage of the customer journey. This competency drastically improves customer satisfaction by establishing a quick communication channel to solve common problems.

Customer service chatbots leveraging NLU are able to:

  • Answer FAQs
  • Simplify checkout and shipping
  • Provide personalized inputs and direction

NLU chatbots allow businesses to address a wider range of user queries at a reduced operational cost. These chatbots can take the reins of customer service in areas where human agents may fall short. For example, a call center that uses chatbots can remain accessible to customers at any time of day. Because chatbots don't get tired or frustrated, they are able to consistently display a positive tone, keeping a brand's reputation intact. NLU can give chatbots a certain degree of emotional intelligence, giving them the capability to formulate emotionally relevant responses to exasperated customers.

Automated ticketing support

The manual management of ticketing can result in a series of inconveniences. These include delays, a countless array of back-and-forth emails, and frustrated customers. Through NLU, these high-volume manual processes can be easily replaced with automatic AI-powered procedures. 

An NLU system capable of understanding the text within each ticket can properly filter and route them to the right expert or department. Because the NLU software understands what the actual request is, it can enable a response from the relevant person or team at a faster speed. The system can provide both customers and employees with reliable information in a timely manner.

While this ability is useful across the board, it particularly benefits the customer service and IT departments. NLU systems are able to flag the most urgent tickets and recommend solutions thanks to their capacity to understand the context and meaning of the different requests they interact with.

Sentiment analysis

Understanding the opinions, needs, and desires of customers is one of the main priorities of organizations and brands. By having tangible information about what customer experiences are positive or negative, businesses can rethink and improve the ways they offer their products and services. NLU-powered sentiment analysis is a significantly effective method of capturing the voice of the customer, extracting emotions from text, and using them to improve customer-brand relationships.

At its most basic, sentiment analysis can identify the tone behind natural language inputs such as social media posts. Taking it further, the software can organize unstructured data into comprehensible customer feedback reports that delineate the general opinions of customers. This data allows marketing teams to be more strategic when it comes to executing campaigns.

Automated document review

Performing a manual review of complex documents can be a very cumbersome, tiring, and time-consuming ordeal. Moreover, mundane and repetitive tasks are often at risk of human error, which can result in dire repercussions if the target documents are of a sensitive nature.

In contrast, NLU systems can review any type of document with unprecedented speed and accuracy. Moreover, the software can also perform useful secondary tasks such as automatic entity extraction to identify key information that may be useful when making timely business decisions.

Business process automation and chatbots

Natural language understanding development services

Botpress allows you to leverage the most advanced AI technologies, including state-of-the-art NLU systems. By using the Botpress open-source platform, you can create NLU-powered chatbots that perform ahead of the curve while costing less money and resources.

All chatbots must be trained before they can be deployed, but Botpress makes this process substantially faster. Chatbots created through Botpress may be able to grasp concepts with as few as 10 examples of an intent, directly impacting the speed at which a chatbot is ready to engage real humans.

In addition, Botpress supports more than 10 languages natively, including English, French, Spanish, Arabic, and Japanese. Users can also take advantage of the FastText model to have access to 157 different languages. Thanks to this, a single chatbot is able to create multi-language conversational experiences and instantly cater to different markets.

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