what’s it for and how does it work?

Today, Artificial Intelligence agencies a multitude of surprising technologies. NLU (Natural Language Understanding) is one of the most successful. Linked to language processing (NLP), it supports organizations in advanced conversational analysis. But what exactly is NLU?

NLU: definition

The NLU is first and foremost an acronym for Natural Language Understandingwhich means understanding of natural language. This technology is linked to AI (Artificial Intelligence) and Machine Learning. As its name suggests, NLU aims to automate the understanding of natural language. As such, it relates directly to natural language technology such as Natural Language Processing. More precisely, it is a branch of NLP.

Its aim is to capture the meaning of a text as a whole. In other words, this AI-based solution is concerned with enabling machines to better understand the overall meaning of words and texts. Explicitly, NLU seeks to understand and interpret connotations and implicit meanings in human communication. This could be emotion, effort, intention or even the precise purpose behind an individual’s statement. That said, this technology is intrinsically linked to conversational analysis and sentimental analysis because it enables the real understanding of statements and writings.

Lhe automatic processing of natural language what is it ?

Natural language processing is a branch of artificial intelligence. It uses machine-learning algorithms to help computers understand natural human language.

On the other hand, NLP is the umbrella term for everything to do with making natural language processing machines. This may involve receiving input, understanding input or generating a response.

NLU and NLP, what’s the difference?

Almost identical, these two technologies are very often confused. In fact, the NLU is a sub-domain of NLP. However, there is a difference between these two concepts. NLP (Natural Language Processing) is aimed at interpret literally the written word or human conversation. The NLU, on the other hand, aims to to identify the intentions as well as the real meaning of writings and statements.

This means that the latter provides a much deeper and more sophisticated understanding than NLP.

How does NLU work?

The first question that springs to mind is: how can a machine correctly interpret the meaning of a piece of writing or a conversation if even a human has difficulty doing so? Here’s the answer. In fact, large volumes of texts and examples are assigned to a machine and its artificial neural networks to enable it to learn. In the case of the NLU, this data supports its knowledge of lexis, grammar and semantics. These examples are also what enable it to discover correlations between words and expressions. Following this learning process, the machine will be perfectly capable of processing an original text and grasping its subtleties.

In order to achieve its mission, natural language understanding uses algorithms to analyze data for the purpose of forming a structured ontology. These algorithms are definitions and concepts that enable relationships to be understood. The technology is then able to recognize the entity and the intention. As a result, it can respond independently of human error, be it a spelling mistake or mispronunciation. With regard to these two types of recognition, here’s how it works:

Intent recognition

This first type of recognition uses natural language processing and machine learning. In fact, it is the first step in understanding the meaning of text and spoken words.

Entity recognition

This type of recognition identifies entities in text or speech. Consequently, machines can understand key information. The NLU therefore comprises two types of entity: numerical and named. Numerical entities are classified by numbers, currencies and percentages. Named entities are classified by people, companies and places.

Using NLU technology

According to all the preceding explanations, the NLU aims to understand the context and deeper meaning of writings as well as words. As such, its domain is particularly focused on the developing strategies and techniques. Furthermore, machine learning, whether rules or statistics, is of great use to NLU technology. In fact, the latter makes it possible to label and categorize content with a human approach.

Together, NLU and machine learning are particularly effective for solving certain tasks. This applies, for example, to content analysis, topic modeling, machine translation and answering relevant questions. Moreover, natural language understanding systems help analysts to divide large volumes of text or spoken comments into coherent groups without having to process them one by one.

Use cases for natural language understanding

It may not be obvious, but NLU is used everywhere. Whether it’s long days spent on a computer, a simple visit to a company page (seeking assistance via a chatbox, for example). These are all cases of interaction with some form of natural language understanding. As a result, most companies use NLU in their chatbots and their artificially intelligent assistants. This enables them to triage customer tickets and understand customer feedback. Forethhough’s customer support AI, for example, uses this technology as part of its understanding process. In particular, it does this before categorizing tickets and suggesting answers to customer concerns. However, natural language understanding isn’t just for customer support.

Routing IVR messages

IVR, or Interactive Voice Responseis a technology that allows incoming callers to use pre-recorded messages and options to send calls to a live operator. Routing strategies can also be implemented. IVR uses speech recognition and NLU to understand a person’s needs.

That said, natural language understanding is therefore an important component of IVR, since interactive voice response takes and processes the caller’s words to take account of their intentions and feelings. IVR is therefore of considerable benefit to customer support teams. As the latter use the telephone as a channel, IVR helps to alleviate the need for agent support.

Data capture

Data capture is a two-stage process. First, it extracts information from paper or electronic documents. Then, it converts it into data for key systems.

In this case, the NLU is indispensable because the captured data must be processed and understood by an algorithm in order to produce the necessary results.

Machine translation

The NLU also plays an important role in machine translation. Also known as automated translation, this is a process whereby computer software translates a language. In particular, it involves translating a text from one language to another without human intervention.

Conversational interfaces

Conversational interfaces are found in particular on the front-end of a Web site. Commonly known as chatbots, they enable customers to enter into a relationship with the company. Conversational interfaces are designed to mimic a human conversation. This is one of the reasons why understanding and processing natural language is crucial to the systems’ ability to do their job.

Assistance and customer service via AI personal assistants

Certainly no one would suspect that customer service AI assistants also use natural language understanding. Indeed, AI and NLU are not only indispensable in chatbots to enable better customer interactions. On the contrary, these two powerful technologies are also used in agent AI assistants. Here, their role is to help support representatives perform their jobs not just better, but more efficiently.

As such, the organization has moved well away from times when the customer couldn’t find certain information that customers demand. Now, they can compile all historical data and information in a single file. This is a folder that sits on a company’s help desk and is capable of generating information based on past and current tickets.

NLU and the future of language

In short, NLU or “Natural Language Understanding” enables machines to understand a set of texts, whether written or spoken. In order to do this, the machine must try to understand the language of the text. To date, NLU and NLP have been used in a wide variety of applications. In fact, the more progress is made, the more these uses will multiply, in all areas.