Natural Language Processing Accelerates as Businesses Expand the Use Cases

In the realm of artificial intelligence, NLU and NLP bring these concepts to life. People can express the same idea in different ways, but sometimes they make mistakes when speaking or writing. They could use the wrong words, write sentences that don’t make sense, or misspell or mispronounce words. NLP can study language and speech to do many things, but it can’t always understand what someone intends to say.

• AI can also have trouble understanding text that contains multiple different sentiments.
• The “processing” piece means that text-based information can be understood in its context and intent can be discerned from unstructured data.
• By way of contrast, NLU targets deep semantic understanding and multi-faceted analysis to comprehend the meaning, aim, and textual environment.
• These use cases will only continue to grow and become higher in demand as the market share for voice and AI speech recognition solutions continues to increase.
• Instead, they want an answer as quickly as possible to make plans accordingly.
• Extractive summarization uses text analytics to take the most important phrases or sentences and stitches together a summarized narrative.

Logic is applied in the form of an IF-THEN structure embedded into the system by humans, who create the rules. This hard coding of rules can be used to manipulate the understanding of symbols. Ecommerce websites rely heavily on sentiment analysis of the reviews and feedback from the users—was a review positive, negative, or neutral? Here, they need to know what was said and they also need to understand what was meant. Gone are the days when chatbots could only produce programmed and rule-based interactions with their users. Back then, the moment a user strayed from the set format, the chatbot either made the user start over or made the user wait while they find a human to take over the conversation.

Conversation Intelligence using NLU, NLP and NLG is the future of customer service and sales

NLU, however, understands the idiom and interprets the user’s intent as being hungry and searching for a nearby restaurant. We’ll also examine when prioritizing one capability over the other is more beneficial for businesses depending on specific use cases. By the end, you’ll have the knowledge to understand which AI solutions can cater to your organization’s unique requirements. NLU and NLP work together in synergy, with NLU providing the foundation for understanding language and NLP complementing it by offering capabilities like translation, summarization, and text generation.

Their language (both spoken and written) is filled with colloquialisms, abbreviations, and typos or mispronunciations. NLU is an area of artificial intelligence that allows an AI model to recognize this natural human speech — to understand how people really communicate with one another. Natural language understanding is particularly difficult for machines when it comes to opinions, given that humans often use sarcasm and irony. Sentiment analysis, however, is able to recognize subtle nuances in emotions and opinions ‒ and determine how positive or negative they are. Insightful data can also be acquired thereby strengthening the company’s business and reducing dissatisfaction from potential customers. This technology has been around over the years and continuously improved the life quality of people from all walks of life or industries especially in the field of business.

Challenges of not using NLP and NLU in your business operations

To some extent, it is also possible to auto-generate long-form copy like blog posts and books

with the help of NLP algorithms. Common devices and platforms where NLU is used to communicate with users include smartphones, home assistants, and chatbots. These systems can perform tasks such as scheduling appointments, answering customer support inquiries, or providing helpful information in a conversational format. Natural Language Understanding is a crucial component of modern-day technology, enabling machines to understand human language and communicate effectively with users.

For example, given the sentence “Jon Doe was born in Paris, France.”, a relation classifier aims

at predicting the relation of “bornInCity.” Relation Extraction is the key component for building relation knowledge

graphs. It is crucial to natural language processing applications such as structured search, sentiment analysis,

question answering, and summarization. In summary, NLU is critical to the success of AI-driven applications, as it enables machines to understand and interact with humans in a more natural and intuitive way. By unlocking the insights in unstructured text and driving intelligent actions through natural language understanding, NLU can help businesses deliver better customer experiences and drive efficiency gains.

The most popular transformer architectures include BERT, GPT-2, GPT-3, RoBERTa, XLNet, and ALBERT. Deep learning methods prove very good at text classification, achieving state-of-the-art results on a suite of standard

academic benchmark problems. Another important computational process for text normalization is eliminating inflectional affixes, such as the -ed and

-s suffixes in English. Stemming is the process of finding the same underlying concept for several words, so they should

be grouped into a single feature by eliminating affixes. Reworked, produced by Simpler Media Group, is the world’s leading community of employee experience and digital workplace professionals. Our mission is to advance the careers of our members via high impact knowledge, networking and recognition (awards).

The integration of NLP algorithms into data science workflows has opened up new opportunities for data-driven decision making. NLU is widely used in virtual assistants, chatbots, and customer support systems. NLP finds applications in machine translation, text analysis, sentiment analysis, and document classification, among others. NLU leverages machine learning algorithms to train models on labeled datasets.

Natural Language Generation (NLG)

This is where natural language understanding — a branch of artificial intelligence — comes in. The software searches for keywords in your questions, and then uses specific applications to generate pre-written answers based on the frequency of their usage. Some of the more interesting business applications of NLP include artificial machine learning generated ad copy, said Jenn Halweil of New York City-based Go Beyond.

NLP has been instrumental in streamlining customer support with chatbots, improving search engines with better query understanding, and enabling voice assistants like Siri and Alexa. For IT teams, one good use case for natural language processing is document classification. Such classification might be good for the basic sorting of information, but it can also have uses in security. Natural Language Understanding (NLU) connects with human communication’s deeper meanings and purposes, such as feelings, objectives, or motivation. It employs AI technology and algorithms, supported by massive data stores, to interpret human language.

How NLP and NLU in data analysis will shape your digital future

And natural language processing chatbots are much more versatile and can handle nuanced questions with ease. By understanding the context and meaning of the user’s input, they can provide a more accurate and relevant response. NLP chatbots are powered by natural language processing (NLP) technology, a branch of artificial intelligence that deals with understanding human language. It allows chatbots to interpret the user intent and respond accordingly by making the interaction more human-like. NLP is a powerful tool for extracting meaningful insights in avalanche of unstructured data generated daily – from social media posts to complex medical records.

NLP is increasingly used by companies and organisations of all sizes, as it enables them to analyse and understand human language and propose appropriate responses. The NLP market is predicted reach more than $43 billion in 2025, nearly 14 times more than it was in 2017. Millions of businesses already use NLU-based technology to analyze human input and gather actionable insights. NLU tools should be able to tag and categorize the text they encounter appropriately. Entity recognition identifies which distinct entities are present in the text or speech, helping the software to understand the key information. Named entities would be divided into categories, such as people’s names, business names and geographical locations.

How do I implement an NLU system? Which tools should I use?

The distinction between these two areas is important for designing efficient automated solutions and achieving more accurate and intelligent systems. Though looking very similar and seemingly performing the same function, NLP and NLU serve different purposes within the field of human language processing and understanding. The key distinctions are observed in four areas and revealed at a closer look.

NLP can process text from grammar, structure, typo, and point of view—but it will be NLU that will help the machine infer the intent behind the language text. So, even though there are many overlaps between NLP and NLU, this differentiation sets them distinctly apart. Natural languages are different from formal or constructed languages, which have a different origin and development path. For example, programming languages including C, Java, Python, and many more were created for a specific reason. Latin, English, Spanish, and many other spoken languages are all languages that evolved naturally over time. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

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

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