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

By 2025, the NLP market is expected to surpass $43 billion–a 14-fold increase from 2017. Businesses worldwide are already relying on NLU technology to make sense of human nlu definition input and gather insights toward improved decision-making. With today’s mountains of unstructured data generated daily, it is essential to utilize NLU-enabled technology.

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. For example, after training, the machine can identify “help me recommend a nearby restaurant”, which is not an expression of the intention of “booking a ticket”. With the abundance of unstructured textual data, extracting valuable information can be a daunting task.

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Using complex algorithms that rely on linguistic rules and AI machine training, Google Translate, Microsoft Translator, and Facebook Translation have become leaders in the field of “generic” language translation. Generally, computer-generated content lacks the fluidity, emotion and personality that makes human-generated content interesting and engaging. However, NLG can be used with NLP to produce humanlike text in a way that emulates a human writer. This is done by identifying the main topic of a document and then using NLP to determine the most appropriate way to write the document in the user’s native language.

• 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.
• This data helps virtual assistants and other applications determine a user’s intent and route them to the right task.
• It should be able  to understand complex sentiment and pull out emotion, effort, intent, motive, intensity, and more easily, and make inferences and suggestions as a result.
• This process is experimental and the keywords may be updated as the learning algorithm improves.
• For example, the discourse analysis of a conversation would focus on identifying the main topic of discussion and how each sentence contributes to that topic.

Embracing NLU is not merely an option but a necessity for enterprises seeking to thrive in an increasingly interconnected and data-rich world. In the realm of artificial intelligence (AI), language serves as a formidable tool, enabling seamless interactions between humans and machines. One crucial aspect that empowers AI to comprehend human language is natural language understanding (NLU).

Natural Language Understanding – NLU NLI

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. Numeric entities would be divided into number-based categories, such as quantities, dates, times, percentages and currencies. NLU helps computers to understand human language by understanding, analyzing and interpreting basic speech parts, separately. In fact, according to Accenture, 91% of consumers say that relevant offers and recommendations are key factors in their decision to shop with a certain company.

This technology allows your system to understand the text within each ticket, effectively filtering and routing tasks to the appropriate expert or department. Also, NLU can generate targeted content for customers based on their preferences and interests. This targeted content can be used to improve customer engagement and loyalty. In this step, the system looks at the relationships between sentences to determine the meaning of a text. This process focuses on how different sentences relate to each other and how they contribute to the overall meaning of a text. For example, the discourse analysis of a conversation would focus on identifying the main topic of discussion and how each sentence contributes to that topic.

Examples of Natural Language Processing in Action

Basically, the machine reads and understands the text and “learns” the user’s intent based on grammar, context, and sentiment. Essentially, it’s how a machine understands user input and intent and “decides” how to respond appropriately. Business applications often rely on NLU to understand what people are saying in both spoken and written language. This data helps virtual assistants and other applications determine a user’s intent and route them to the right task. Explore some of the latest NLP research at IBM or take a look at some of IBM’s product offerings, like Watson Natural Language Understanding. Its text analytics service offers insight into categories, concepts, entities, keywords, relationships, sentiment, and syntax from your textual data to help you respond to user needs quickly and efficiently.

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In NLU, they are used to identify words or phrases in a given text and assign meaning to them. One of the major applications of NLU in AI is in the analysis of unstructured text. Natural Language Processing (NLP) is a technique for communicating with computers using natural language. Because the key to dealing with natural language is to let computers “understand” natural language, natural language processing is also called natural language understanding (NLU, Natural). On the one hand, it is a branch of language information processing, on the other hand it is one of the core topics of artificial intelligence (AI).

Accurately translating text or speech from one language to another is one of the toughest challenges of natural language processing and natural language understanding. While both understand human language, NLU communicates with untrained individuals to learn and understand their intent. In addition to understanding words and interpreting meaning, NLU is programmed to understand meaning, despite common human errors, such as mispronunciations or transposed letters and words.

Similarly, spoken language can be processed by devices such as smartphones, home assistants, and voice-controlled televisions. NLU algorithms analyze this input to generate an internal representation, typically in the form of a semantic representation or intent-based models. Natural Language Understanding (NLU) refers to the ability of a machine to interpret and generate human language.

Natural language processing works by taking unstructured data and converting it into a structured data format. For example, the suffix -ed on a word, like called, indicates past tense, but it has the same base infinitive (to call) as the present tense verb calling. NLU is a branch ofnatural language processing (NLP), which helps computers understand and interpret human language by breaking down the elemental pieces of speech.

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Natural Language Understanding (NLU) or Natural Language Interpretation (NLI) is a sub-theme of natural language processing in artificial intelligence and machines involving reading comprehension. Natural language understanding is considered a problem of artificial intelligence. In the realm of customer service, NLU-powered chatbots are transforming the way companies engage with their clients. These AI-driven virtual assistants can interpret customer queries, address concerns, and provide relevant solutions promptly and accurately.

Trying to meet customers on an individual level is difficult when the scale is so vast. Rather than using human resource to provide a tailored experience, NLU software can capture, process and react to the large quantities of unstructured data that customers provide at scale. Natural Language Generation is the production of human language content through software.

Natural Language Understanding deconstructs human speech using trained algorithms until it forms a structured ontology, or a set of concepts and categories that have established relationships with one another. This computational linguistics data model is then applied to text or speech as in the example above, first identifying key parts of the language. GLUE and its superior SuperGLUE are the most widely used benchmarks to evaluate the performance of a model on a collection of tasks, instead of a single task in order to maintain a general view on the NLU performance. They consist of nine sentence- or sentence-pair language understanding tasks, similarity and paraphrase tasks, and inference tasks.

Expert.ai Answers makes every step of the support process easier, faster and less expensive both for the customer and the support staff. To understand such many different expressions is a challenge to the machine. In the past, machines could only deal with “structured data” (such as keywords), which means that if you want to understand what people are talking about, you must enter the precise instructions. Depending on your business, you may need to process data in a number of languages.