Why should every business start using natural language understanding?

Ninety-one percent customer retention? Easy: this amount of consumers will return for more purchases from companies that provide relevant offers and recommendations, Accenture reports. Consequently, personalized customer experience should become a significant part of commercial business success.

But how to deliver a tailored customer experience if your resources are limited? Artificial intelligence helps here: natural language processing (NLP) experts can teach machines to understand people with the help of natural language understanding (NLU) techniques. These tools enable business scaling; contrarily, ignoring NLU instruments in your business limits the customer experience you can provide.

The sea is still full of fish: researchers have discovered that more than 60% of consumers feel that businesses need to care more about them. Also, don’t forget about 4.95 billion Internet users globally who likely prefer and anticipate natural language understanding in their experience.

These factors point to the necessity of NLU implementation. So, let’s dive deeper into NLU definition and purpose.

What is natural language understanding?

Natural language understanding (NLU) is a field of computer science that analyzes the meaning and concepts behind entire human speech or text rather than simply what separate words mean.

Natural language understanding aims to perceive multiple implications and connotations innate in human communication, such as the intent, sentiment, or goal behind a statement. It utilizes algorithms, machine learning, and AI supported by numerous data libraries to understand our language.

Rather than relying on computer commands and programming language syntax, natural language understanding enables machines to accurately grasp and answer the meaning and emotions expressed in natural language content.

Natural language processing (NLP) vs. natural language understanding (NLU) vs. natural language generation (NLG)

Natural language understanding (NLU) is mainly used to describe an AI that can interpret and comprehend human language (e.g., English, Spanish, Chinese). In contrast, natural language processing (NLP) is often used as an umbrella term for the entire process of translating unstructured data of human-produced content into structured data and engaging technology in human-language communication.

Natural language processing aims to create systems to understand human language, whereas natural language understanding seeks to establish comprehension.
Technically, NLU is a subset field of NLP, using linguistic features and structures mapped out by NLP.

Natural language generation (NLG) systems produce human language texts or speech through computer software and algorithms. In other words, it translates structured data into a language humans can understand. Usually, it extends a process started with NLU that generates responses.

In contrast to overcomplicated technologies like GPT-3, NLG systems such as Siri, Alexa, and any voice assistant fill templates and generate text deterministically rather than use some generative language models. First, they run natural language processing algorithms to understand spoken words. Then (or in parallel), they apply a natural language understanding system to judge the intention behind the query. Afterward, it is the turn of the natural language generation application, answering questions in a human-like manner.

Both NLU and NLG are NLP techniques and could be applied together or separately.

How does natural language understanding (NLU) work?

Due to congenital linguistic subtleties, people sometimes struggle to understand their own language. As a result, even two people may listen to or read the exact text and walk away with entirely different interpretations, illustrating why dealing with unstructured data is a challenge for machines. Hence, the NLU algorithm must first structure the piece of content by extracting information from the source of verbal or textual information.

Let’s take a request typed into a search engine as an example of unstructured data: “Where to eat in London after 10 pm”

What the machine, with the help of NLU, understands: “eat” is intent; “London” is location, and “10 pm” is time.

An NLU algorithm deconstructs human speech until it forms a structured ontology consisting of a set of ideas, concepts, and categories with established connections and relations between each other. This computational linguistics data model is then applied to text or speech, as in the example above, first identifying critical parts of the grammatical structure.

Intent and entity recognition are two key concepts in NLP. Let’s use the previous example so that we can grasp the meaning of each type.

Intent recognition identifies what the person speaking or writing intends to do. Identifying their objective helps the software understand the goal of the interaction. In this example, the NLU technology can deduce that the person wants to eat at specific hours, and the most likely mode of it is late dinner. The search engine using NLU would likely respond by returning search results that offer restaurants and cafes that are open late.

Entity recognition establishes which specific entities occur in the content; thus, the software understands the main pieces of information. Generally, named entities are text that could be divided into categories, such as geographical locations and people’s or businesses’ names. Here, “London” is the named entity. Numeric entities could be divided into number-based categories, such as dates, times, quantities, percentages, and currencies. In our example, “10 pm” is the numeric entity.

How do machines learn what we mean?

To understand human language, NLU tools should appropriately tag and categorize the text they encounter. Hence, the NLU algorithms should be sophisticated enough, as implicit factors are challenging to comprehend.

Another essential condition of a successful NLU project is good training. NLU-based machines function well only if their backbone machine-learning algorithms have been adequately trained on a significant dataset.

In our previous example, the NLU-enabled search engine can infer intent because it has learned from interactions made in the past. A dataset of these interactions contains queries collected from actual users and labels indicating the query’s intent. In addition, the named entities, time expressions, et cetera are also labeled, so these could be used for training name entity recognition (NER) models and to facilitate training the intent model itself.

When a user interacts with the system, it can collect explicit feedback such as “Was this response helpful?” answers or implicit feedback, considering it positive if the user acts on the response and negative if they discard it without interacting. This feedback will then be used to reinforce the model’s understanding of the users’ queries.

Taking action and responding

Usually, the main goal of an NLU-based tool is to appropriately answer the query in a way that will satisfy a user. For example, suppose the system has not been trained on sufficiently varied data. In that case, it’s possible that, while we get the information from the query, such as the location and time in the example above, but misclassify the actual intent because the user said it in an unexpected way. This would result in the system providing results for the right time and place but for the wrong action.

Or vice versa, if the NER model is weak, the system could understand the action but not detect the time specification provided by the user. In other words, without being able to detect intent accurately, the natural language understanding system won’t provide the users with the answers they’re looking for.

Using our example, an unsophisticated model could respond by showing data for all types of random restaurants and displaying their working hours rather than links for particular restaurants that serve after 10 pm.

Using our example, an unsophisticated model could respond by showing data for all types of random restaurants and displaying their working hours rather than links for particular restaurants that work after 10 pm.

Why and how could you use NLU technology?

Best practice for natural language understanding

1. The bigger the dataset, the better. However, you do not always have to go crazy with the amount of data, as not all NLU tasks are equally challenging. With more manageable tasks, relatively fewer amounts of data are needed; in contrast, for more complex cases, more data. Talk with an AI expert to understand what is the probable data demand.
2. To define the intents and entities precisely, you should pay attention to labeling.
3. In the case of voice interaction, it is better to record the human voice for common phrases rather than individual words for NLG-produced responses, as the natural transition between words results in a more human experience.
4. A perfect NLU solution is handy and easy enough to use for all your staff, whether or not they are tech.
5. Thinking about how to integrate your NLU solutions with your existing software beforehand will ease management and execution.

Conclusion

NLU has quickly moved from being a fancy tool to something vital, especially for businesses that care about customer support quality or simply wish to get insights from their ever-increasing amount of textual data. Millions of companies have already implemented technologies based on natural language understanding to analyze human input and gather actionable insights. And the number will increase as the market is predicted to grow nearly 14 times its 2017 levels, reaching more than $43 billion by 2025. So, NLU implementation has become a competitive imperative.

Plenty of ready-made NLP and NLU solutions and platforms are available in the market. However, finding an off-the-shelf solution to meet your specific business needs could be challenging. Often, such solutions come with multiple pre-built features you might not need. Additionally, choosing the right solution and a partner company without a solid knowledge of natural language processing could be difficult. All these factors affect the results of what you want out of an NLU solution.