Human language is extremely complicated for a machine to understand. While most computers operate in a world of clearly defined rules and concepts, human communication is fluid, complex, and extremely subtle. While all computers will understand an instance of data input in the same way, two people reading the same phrase may walk away with two completely different interpretations of it.
There can be no be-all end-all rule-based solution to natural language because every person creates the meaning of their own phrases. Thereby, computers must use NLU technology if they are to be capable of grasping and acting on the things we say.
What is an NLU engine?
Also known as natural language interpretation, natural language understanding (NLU) is a data science competency that allows artificial intelligence to understand human communication.
NLU is usually implemented with written information but thanks to text-to-speech recognition software, it is possible for AI with NLU capabilities to understand verbal communication. Due to its capacity for quick natural language recognition, NLU is very useful in all kinds of industries. In recent years, it has accrued considerable commercial interest.
NLU is often implemented in tandem with natural language generation (NLG). While the former enhances the comprehension capabilities of AI, the latter gives computers the capacity to generate meaningful data without the need for human intervention. Together, these two competencies allow artificial intelligence to understand what people say and answer back coherently.
How does natural language understanding (NLU) work?
NLU works by breaking human communication down into basic concepts that can be understood individually. These concepts are then reinterpreted by the software, which analyzes the relationship between words to establish a clear message. This is made possible thanks to NLU machine learning algorithms.
A language model is used instead of a set of static rules to teach NLU engines how to recognize and make sense of human speech. Through various statistics and probabilistic techniques taught using millions of data points, language models give NLU machines the capacity to predict verbal outcomes, answer questions, and even translate data into additional languages.
Basic natural language text concepts understood through NLU include locations, and dates. For example, in the sentence “Harry met Sally in the park last Friday at 6 pm”, an NLU software solution will be able to recognize “the park” as a location, “last Friday” as a date, and “6 pm” as time. At the same time, the software will acknowledge Harry and Sally as entities.
An entity in NLU is any word or phrase that can be used to add additional context to a message. NLU entities can be people, objects, locations, or even abstract ideas. Data points containing numbers are known as numeric entities. Examples of these include quantities, dates, times, currencies, and percentages.
While the awareness of entities in a body of text may be remarkable, the true wonder of NLU is its capacity for intent classification. Through this competency, an NLU-powered machine is able to recognize what people are trying to achieve. This way, NLU can be used to improve customer service, sales, and many other business undertakings.
NLU recognizes correct intents similarly to how a function is recognized in programming. For instance, an NLU-powered AI that reads the phrase “I would like to buy a cup of coffee” may convert it to a single intent such as “purchaseCoffee”. In a business setting, intent classification turns AIs into virtual assistants capable of following instructions and performing routine tasks simultaneously.
What is the difference between NLU and NLP?
Natural language understanding (NLU) is a subset of natural language processing (NLP). NLP is a field that incorporates both linguistics and computer science to improve the communication between humans and AI. Meanwhile, NLU is the discipline within NLP that specifically deals with AI’s capacity to understand human speech.
The impact of NLU in customer experience
NLU has had a massive impact on customer communication. The customer experience can be substantially improved thanks to fast and powerful AI calculations, which create a seamless conversational flow between brands and consumers.
Research shows that more than two thirds of American consumers are still reluctant to do business with impersonal software. Over 80% of the top-performing companies report that the improvement of the digital human experience is a major priority (source).
When interacting with a chatbot powered via NLU, customers can use their natural language to communicate their thoughts, ideas, needs, and desires. The software can be given a wide range of applications, from facilitating menu navigation to state-of-the-art data collection.
Businesses that implement NLU technology are at a considerable advantage over the competition. The software eliminates the need for a human agent to be present during most of the communication. Moreover, NLU can be deployed through various communication channels like SMS, Messenger, Twitter, and WhatsApp, giving users the chance to receive NLU-powered services via the application of their choice.
NLU examples and applications
Enhanced customer support
A straightforward but nonetheless revolutionary application of NLU is the improvement of customer service operations. NLU-powered chatbots can offer immediate and seamless customer reports at any time of day and in multiple languages. This allows companies to cater to customer needs regardless of their mother tongue, geographic location, or time zone.
Interactive voice response (IVR)
IVR technology is able to improve a business’s phone system infrastructure. Companies can leverage IVR to provide customers with voice assistant software that interacts with them, gathers information, and performs tasks based on customer feedback. If certain assignments are too complex for the assistant, the software is able to enable fluent communication between a caller and a human agent.
Message routing
Message routing allows companies to connect different message channels. Information input received by a system can be captured by natural language API overview methods that recognize its significance and republish it on relevant communication channels. For instance, content-based message routing can be used to spread an inventory check across multiple physical systems.
Data Capture
Software solutions equipped with machine learning competencies such as NLU have been a game changer when it comes to the gathering of data. Modern data environments are too large for a human or team to feasibly analyze, while NLU-powered machines can do it in the blink of an eye.
Automatic reasoning
This computer science competency produces applications that allow machines to understand different aspects of reasoning. Programs leveraging automatic reasoning can solve problems related to formal logic, logic programing, mathematics, and more. Due to its abstract nature, automated reasoning has connections to philosophy and theoretical computer science while still being considered a subfield of artificial intelligence.
Conversational artificial intelligence
Conversational interfaces implement the latest in neural network technology to mimic the way humans think. These AI solutions are supported by millions of data points gathered via training data, fine-tuning their capacity to communicate with people. NLU engine benchmarking operations give computers the information required to converse with someone without them even knowing that they are not communicating with a real person.
What is conversational AI and how does it work?
Sentiment analysis (SA)
Sentiment analysis reads into user intent to recognize whether they have a positive, negative, or neutral opinion about any kind of subject. When NLU and SA are used together, a coherent report of customers’ thoughts and feelings can be accessed. Companies implement NLU-guided sentiment analysis operations to gauge the popularity and success of their products and services in the online sphere.
Machine translation (MT)
Also referred to as robotized interpretation, machine translation lets AI translate a body of text into multiple languages without human intervention. Some applications contain basic, rule-based MT capabilities, where atomic words are replaced by their counterparts in another language. However, NLU provides the framework to leverage neural machine translation (NMT), which simulates the human brain to translate data based on statistical models.
Natural language understanding engine development services
Reap all the benefits of avant-garde NLU technology with the help of Botpress. The native NLU capabilities of Botpress run on-premise and support multiple languages, allowing companies to massively increase their outreach with minimal use of resources. Botpress NLU data can also be synced with third-party solutions to personalize the way businesses implement their data.
Getting started with Botpress is quick, easy, and free. You can sign up for a free account to access the editor and begin developing avant-garde conversational AI applications. Likewise, Botpress Enterprise provides teams with an open-source platform to build scalable, secure, and powerful enterprise chatbots.
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