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From NLP to NLU: what’s the added value?

What is the difference between NLP and NLU: Business Use Cases

This is a powerful cognitive ability that supports many business processes and increases human capability. For example, the Port of Montreal used NLP and AI models to detect and distribute important cargo during the most difficult months of the pandemic in 2020. NLP-enabled solutions took care of tedious or repetitive manual “reading” operations to extract insights and support human decision-making. But delivering personalized messaging at scale across channels is hard to do using only human creative talent. To address the challenge, companies are turning to Generative AI to automate the creation of various forms of content and personalize them for granular audiences.

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. 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.

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Robotic process automation (RPA) is an exciting software-based technology which utilises bots to automate routine tasks within applications which are meant for employee use only. Many professional solutions in this category utilise NLP and NLU capabilities to quickly understand massive amounts of text in documents and applications. What’s more, you’ll be better positioned to respond to the ever-changing needs of your audience. Verbit combines the efficiency of artificial intelligence with the expertise of professional human transcribers to offer captions and transcripts with accuracy rates as high as 99%. Another key difference between these three areas is their level of complexity.

What is the difference between NLP and NLU: Business Use Cases

NLU technology can also help customer support agents gather information from customers and create personalized responses. By analyzing customer inquiries and detecting patterns, NLU-powered systems can suggest relevant solutions and offer personalized recommendations, making the customer feel heard and valued. Natural Language Understanding (NLU) plays a crucial role in the development and application of Artificial Intelligence (AI).

What are NLP, NLU, and NLG?

Personalization has been heralded as the key to success for businesses in every industry. Studies show that organizations outperforming their competition attribute 40% of the additional revenue to their personalization efforts. This was an early example of a closed-domain answering system built in the 1960s, which was answer questions on one year’s worth of baseball stats and facts. You can ask this system simple questions like, “What is this person’s batting average?” and it will return with the correct and accurate answer. For instance, the word “bank” could mean a financial institution or the side of a river.

What is the difference between NLP and NLU: Business Use Cases

It aims to highlight appropriate information, guess context, and take actionable insights from the given text or speech data. The tech builds upon the foundational elements of NLP but delves deeper into semantic and contextual language comprehension. On the other hand, natural language understanding is concerned with semantics – the study of meaning in language. NLU techniques such as sentiment analysis and sarcasm detection allow machines to decipher the true meaning of a sentence, even when it is obscured by idiomatic expressions or ambiguous phrasing.

The difference between NLU, NLP, and NLG

Additionally, sentiment analysis uses NLP methodologies to determine the sentiment and polarity expressed in text, providing valuable insights into customer feedback, social media sentiments, and more. The steps involved in natural language processing start with having access to data in its original form (a written message in a database, for example) and a language base to compare it with. This gives you a better understanding of user intent beyond what you would understand with the typical one-to-five-star rating.

What is the difference between NLP and Use Cases

It involves understanding the intent behind a user’s input, whether it be a query or a request. NLU-powered chatbots and virtual assistants can accurately recognize user intent and respond accordingly, providing a more seamless customer experience. Your software can take a statistical sample of recorded calls and perform speech recognition after transcribing the calls to text using machine translation. The NLU-based text analysis can link specific speech patterns to negative emotions and high effort levels. Using predictive modeling algorithms, you can identify these speech patterns automatically in forthcoming calls and recommend a response from your customer service representatives as they are on the call to the customer.

Using our example, an unsophisticated software tool could respond by showing data for all types of transport, and display timetable information rather than links for purchasing tickets. Without being able to infer intent accurately, the user won’t get the response they’re looking for. Natural Language Understanding is a subset area of research and development that relies on foundational elements from Natural Language Processing (NLP) systems, which map out linguistic elements and structures.

What is the difference between NLP and NLU: Business Use Cases

In NLU systems, this output is often generated by computer-generated speech or chat interfaces, which mimic human language patterns and demonstrate the system’s ability to process natural language input. Natural Language Understanding (NLU) refers to the process by which machines are able to analyze, interpret, and generate human language. Natural Language Understanding (NLU) refers to the ability of a machine to interpret and generate human language.

On the other hand, NLU delves deeper into the semantic understanding and contextual interpretation of language. It goes beyond the structural aspects and aims to comprehend the meaning, intent, and nuances behind human communication. NLU tasks involve entity recognition, intent recognition, sentiment analysis, and contextual understanding. By leveraging machine learning and semantic analysis techniques, NLU enables machines to grasp the intricacies of human language.

Computers don’t have brains, after all, so they can’t think, learn or, for example, dream the way people do. Once the data informs the language model, you can analyze the results to determine whether they’re sufficiently accurate and comprehensive. If the results are unsatisfactory upon analysis, you’ll need to adjust the input data before trying again. With Akkio’s intuitive interface and built-in training models, even beginners can create powerful AI solutions. Beyond NLU, Akkio is used for data science tasks like lead scoring, fraud detection, churn prediction, or even informing healthcare decisions.

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Read more about What is the difference between NLP and Use Cases here.

  • These models, trained on extensive datasets, excel in generating human-like text and understanding complex language patterns.
  • Tools like Grammarly, for example, use NLP to help you improve your writing, by detecting grammar, spelling, or sentence structure errors.
  • Ernie Smith is a former contributor to BizTech, an old-school blogger who specializes in side projects, and a tech history nut who researches vintage operating systems for fun.
  • However, navigating the complexities of natural language processing and natural language understanding can be a challenging task.

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