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NLU vs NLP in 2024: Main Differences & Use Cases Comparison

Cem Dilmegani
Updated on Feb 5
5 min read
NLU vs NLP in 2024: Main Differences & Use Cases ComparisonNLU vs NLP in 2024: Main Differences & Use Cases Comparison

Have you ever wondered how Alexa, ChatGPT, or a customer care chatbot can understand your spoken or written comment and respond appropriately? NLP and NLU, two subfields of artificial intelligence (AI), facilitate understanding and responding to human language. Both of these technologies are beneficial to companies in various industries.

Although NLP and NLU can be confused with each other, they are not the same and the differences between them make one capacity more essential than another for specific use cases (see Figure 1). The distinctions between NLP and NLU can therefore influence which technology providers businesses should work with to develop suitable AI solutions that automate particular jobs. To reduce the information gap, in this article, we will cover:

  1. Definition and principles of NLP.
  2. Definition and principles of NLU.
  3. The main difference between NLU and NLP after introducing both concepts. 
  4. When NLU or NLP capabilities become more significant for particular use cases.
  5. Top 10 NLP vs NLU use cases comparison from 5 different industries. 

Definition & principles of natural language processing (NLP)

Natural language processing (NLP) is a field of AI. It enables computers to evaluate and organize unstructured text or speech input in a meaningful way that is equivalent to both spoken and written human language. 

NLP models benefit from machine learning and deep learning techniques when it comes to completing tasks like language translation or interpreting questionnaire forms to identify suitable mortgage plans for customers.

Designing effective NLP models depends on a multidisciplinary team that constitutes:

  • Computer scientists
  • Data scientists 
  • And linguists.

Since human language is complicated, standardized input is necessary for NLP training. Data pre-processing is used to create standardized input which includes following techniques:

Data pre-processing aims to divide the natural language content into smaller, simpler sections. ML algorithms can then examine these to discover relationships, connections, and context between these smaller sections. Consider Figure 2 as an example. NLP links Paris to France, Arkansas, and Paris Hilton, as well as France to France and the French national football team. Thus, NLP models can conclude that “Paris is the capital of France” sentence refers to Paris in France rather than Paris Hilton or Paris, Arkansas.

Figure 2: Example of data pre-processing.

Image shows how entity linking makes NLP models to understand content.

Definition & principles of natural language understanding (NLU)

Natural language understanding (NLU), is a subfield of NLP. It is the technology behind the intent recognition. NLU models use syntactic and semantic analysis to comprehend actual meaning and sentiment of human language. Thus, NLU models reacts:

  • Grammatical structure of the sentences.
  • Emotional situation of the people.
  • Wording selection.
  • Satire and metaphoric explanation.
  • Tone and accent.

Intent recognition and sentiment analysis are the main outcomes of the NLU. Thus, it helps businesses to understand customer needs and offer them personalized products.

For those interested, here is our benchmarking on the top sentiment analysis tools in the market.

What is the main difference between NLU and NLP?

NLP models transform unstructured data into structured data. As a result, algorithms search for associations and correlations to infer what the sentence’s most likely meaning is rather than understanding the genuine meaning of human languages.

However, NLU lets computers understand “emotions” and “real meanings” of the sentences.

As an analogy we can use the difference between translation and transcreation:

  • Translation is what NLP models do. They translate the input word by word.
  • Transcreation is what NLU models do. rewriting input text so that speakers of many languages can understand it in its entirety. Transcreation is the exact opposite of word-for-word translation in some circumstances (such as when translating proverbs).

Let’s illustrate this example by using a famous NLP model called Google Translate. As seen in Figure 3, Google translates the Turkish proverb “Damlaya damlaya göl olur.” as “Drop by drop, it becomes a lake.” This is an exact word by word translation of the sentence. 

The exact meaning of the proverb is: “Over time, even tiny sums of money saved add up to wealth.” or “Many a mickle makes a muckle.” for native English speakers. A well trained NLU model might translate in these ways.

Figure 3: Insufficiency of word by word translation of NLP models. 

An example of differences between natural language understanding and natural language processing.

Which natural language capability is more crucial for firms at what point?

Sentiment analysis and intent identification are not necessary to improve user experience if people tend to use more conventional sentences or expose a structure, such as multiple choice questions. Consequently, an NLP model might be able to automate processes.

NLU skills are necessary, though, if users’ sentiments vary significantly or if AI models are exposed to explaining the same concept in a variety of ways. 

Top 10 NLP vs NLU use cases compression with examples

In this section, we will introduce the top 10 use cases, of which five are related to pure NLP capabilities and the remaining five need for NLU to assist computers in efficiently automating these use cases. Figure 4 depicts our sample of 5 use cases in which businesses should favor NLP over NLU or vice versa.

Figure 4: NLP vs NLU use cases comparison.

Image illustrates when NLP is preferable to NLU for special use cases, or vice versa.

E-commerce use cases

1. Frequently asked questions (FAQs) chatbot

With FAQ chatbots, businesses can reduce their customer care workload (see Figure 5). FAQs are, by definition, a collection of commonly asked questions. As a result, they do not require both excellent NLU skills and intent recognition.

Figure 5: An example of a FAQ chatbot in action.

Image is a screenshot of a conversation between FAQ chatbot and a customer.
Source: Haptik

2. Product discovery via natural language

Sometimes people know what they are looking for but do not know the exact name of the good. In such cases, salespeople in the physical stores used to solve our problem and recommended us a suitable product. In the age of conversational commerce, such a task is done by sales chatbots that understand user intent and help customers to discover a suitable product for them via natural language (see Figure 6).

Mimicking salespeople on digital channels definitely requires a well-trained NLU.

Figure 6:  Real life example of product discovery via natural language.

Image shows an example of prodıct discovery with natural language.
Source: Haptik

Healthcare use cases

3. Diagnosis of the patients

Questionnaires about people’s habits and health problems are insightful while making diagnoses. Such answers are predictable, so NLP models can interpret them. As a result, they assist in determining the patients’ health issues.

4. Detect fraudulent claims regarding health services

False patient reviews can hurt both businesses and those seeking treatment. Sentiment analysis, thus NLU, can locate fraudulent reviews by identifying the text’s emotional character. For instance, inflated statements and an excessive amount of punctuation may indicate a fraudulent review.

Insurance use cases

5. Underwriting (risk assessment)

Questionnaires are used by insurers to calculate premium costs. For instance, the address of the home a customer wants to cover has an impact on the underwriting process since it has a relationship with burglary risk. NLP-driven machines can automatically extract data from questionnaire forms, and risk can be calculated seamlessly.

6. Claims processing

When an unfortunate incident occurs, customers file a claim to seek compensation. As a result, insurers should take into account the emotional context of the claims processing. As a result, if insurance companies choose to automate claims processing with chatbots, they must be certain of the chatbot’s emotional and NLU skills.

Banking use cases

7. Calculating mortgage rates

The procedure of determining mortgage rates is comparable to that of determining insurance risk. Forms give banks useful information. Information can be retrieved from these forms using NLP models. As demonstrated in the video below, mortgage chatbots can also gather, validate, and evaluate data.

8. Wealth management assistant

Before digitalization, people went to financial institutions and provide information regarding their:

  • Desired returns.
  • Risk attitudes
  • Desired investment period.

Most of the time financial consultants try to understand what customers were looking for since customers do not use the technical lingo of investment. Nowadays, wealth management chatbots can do the same thing. Since customers’ input is not standardized, chatbots need powerful NLU capabilities to understand customers.

Hospitality use cases

9. Automate customer service tasks

Before booking a hotel, customers want to learn more about the potential accommodations. People start asking questions about the pool, dinner service, towels, and other things as a result. Such tasks can be automated by an NLP-driven hospitality chatbot (see Figure 7).

Figure 7: Chatbot automates customer service tasks for a hotel.

Image shows how a chatbot solves a customer query.
Source: Haptik

10. Personalized travel recommendation

By considering clients’ habits and hobbies, nowadays chatbots recommend holiday packages to customers (see Figure 8). Since it is not a standardized conversation, NLU capabilities are required.

Figure 8: Chatbot recommends a customized vacation.

Image shows a conversation between a user and chatbot where chatbot recommends a personalized vacation plan.
Source: Haptik

Note: For each application we cover above, a combination of NLP with NLU provides better results. 

To learn about the future expectations regarding NLP you can read our Top 5 Expectations Regarding the Future of NLP article.

If you have any additional queries about the distinctions between NLU and NLP, you may contact us at:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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