Why should every business start using natural language understanding?

Irina Kolesnikova
November 7th, 2022

natural language understanding concept

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) service 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.

How NLP, NLU and NLG are related
Source: techtarget.com

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.

NLU deconstructs the request

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.

How NLU works simplified

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.

NLU-powered search result

Why is natural language understanding essential for business?

The large volumes of natural language content that businesses produce grow daily, making it resource-demanding to process all those unstructured data. NLU develops techniques and strategies to efficiently process and understand the meaning and context of this input at scale. NLU tools allow data scientists to process large volumes of natural language text into coherent groups without reading them all.

This provides businesses with solutions to analyze content, translate text, and answer questions at a scale unachievable with human effort alone. NLU-enabled technology will get the most out of textual and verbal information and help respond to customers in a way they will appreciate.

NLU enhances and speeds up content analysis

Human language is difficult for computers to grasp, as it is complex, fluid, and full of nuances.
However, NLU enables many types of technology to understand natural language content on a level similar to humans, down to detecting incorrect naming or other typing errors, but at inhuman speed and scale. Thus, the content processing goes much more efficiently.

NLU can reduce costs

Let’s look at how NLU-based technology could lower customer service costs and improve customer satisfaction.

case study example from elisa

An AI-based chatbot software has helped Elisa, one of the leading telecommunications companies in Northern Europe, resolve more customer issues.

The company has over 500 customer service agents, working hard to serve around 100,000 incoming contacts per month, all channels combined.

The AI-powered chatbot enabled the company to meet changing customer expectations and build synergies between product management and customer service departments.

As a result, 45% of all the inbound contacts are now successfully handled by the chatbot Annika.

The voice assistant application can minimize costs per contact by saving human agents’ time. For instance, an algorithm can use a statistical sample of recorded calls and transcribe the calls with speech recognition. Then, the NLU-based tool can perform sentiment analysis of customer feedback and link subjects and topics with specific language patterns of negative emotions, providing agents with meaningful insights. Thus, they are ready to meet customers’ expectations, not spend time on extra preparations.

Why and how could you use NLU technology?

Among many other cases, businesses can use natural language understanding for:

  • Chatbots
  • Call automation bots
  • Voice assistants
  • Conversational IVR (Interactive Voice Response) systems for calls
  • Internet search queries
  • Internal and external automated email responses
  • Social media comments
  • Extract information from legal documents/manuals

 

We created a guide on natural language processing use cases, some of which include natural language understanding technology. For further insight, you can find the most common NLU benefits below.

Automated actions

NLU solutions can create a highly interdependent input-and-response system, allowing input phrases to trigger actions automatically. Thus, it makes the entire process faster and less resource-demanding while freeing human employees from repetitive and time-consuming tasks.

Quick response

Being able to process unstructured text rapidly provides you the superpower of answering questions instantly in a customer-first way. To succeed, your machine learning team must make an NLU system to parse and analyze texts and then provide suggestions at scale and speed.

Multiple language support

Depending on goals, businesses might need to analyze data in several languages. Either machine translation or algorithms trained in many languages can help establish a more effective process.

In-depth analysis

As sophisticated NLU solutions rely on training data and content analysis, they can recognize entities and their relationships. Artificial intelligence can make inferences and suggestions by understanding complex implicit sentiments alongside intents and motives behind the natural language content. Having a continuous machine learning process, the AI can be trained to predict the result of interaction early and make suggestions to resolve it the best way. For example, the MindTitan team built a solution that sifts thousands of texts to find signs of public money misuse. The system conducts in-depth analysis to find patterns that are hard to identify for humans due to patterns’ complexity alongside the enormous amount of data to go through.

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 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.

Custom NLP services will address those issues. In addition, a good development partner will accommodate any special needs you might have throughout the project.

With a customized solution, your software partner will understand your business and technical requirements and help you figure out the best way to solve the problem.

Among its many other advantages, a custom NLP solution will yield the most accurate results, benefiting your business in terms of finance and reputation.

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