What’s the Difference Between NLU and NLP?

How NLU Enhances Customer Experience

If you are new to this technology, this guide will help you understand the different natural language automation tools available, how they work, and how they will help companies transform information delivery. Another area of advancement in NLP, NLU, and NLG is integrating these technologies with other emerging technologies, such as augmented and virtual reality. As these technologies continue to develop, we can expect to see more immersive and interactive experiences that are powered by natural language processing, understanding, and generation.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. 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. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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It is used, for example, in marketing to identify potential buyers by analysing their online behaviour. Chatbots also use this technology to manage standard tasks such as providing information on products or services, answering questions, etc. NLP is widely used for text classification, character recognition, automatic correction and automatic summarisation.

Businesses will invest more in NLP techniques to build systems that would be unsupervised, effortless, and be able to interact successfully in a human-like manner. For example, the chatbot could say, “I’m sorry to hear you’re struggling with our service. I would be happy to help you resolve the issue.” This creates a conversation that feels very human but doesn’t have the common limitations humans do. To generate text, NLG algorithms first analyze input data to determine what information is important and then create a sentence that conveys this information clearly.

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

In banking, NLP offers numerous benefits, such as enhancing customer service through AI-driven chatbots that provide quick, accurate responses to inquiries. It aids in analyzing financial documents for risk assessment, detecting fraudulent activities through transaction pattern analysis, and personalizing banking services based on customer data analysis. NLP technologies can also streamline compliance and reporting processes, making banking operations more efficient and customer-centric. Statistical models marked a significant shift, utilizing probabilities to predict language patterns. These models analyzed large text corpora to discern linguistic trends, allowing for more adaptive and context-aware language processing.

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. In this step, the system extracts meaning from a text by looking at the words used and how they are used.

Customer insights: Sentiment analysis and market understanding`

Those insights can help you make smarter decisions, as they show you exactly what things to improve. NLG uses algorithms to solve the extremely difficult problem of turning data into understandable writing. Natural language output, on the other hand, is the process by which the machine presents information or communicates with the user in a natural language format. This may include text, spoken words, or other audio-visual cues such as gestures or images.

We would love to have you on board to have a first-hand experience of Kommunicate. The verb that precedes it, swimming, provides additional context to the reader, allowing us to conclude that we are referring to the flow of water in the ocean. The noun it describes, version, denotes multiple iterations of a report, enabling us to determine that we are referring to the most up-to-date status of a file. To have a clear understanding of these crucial language processing concepts, let’s explore the differences between NLU and NLP by examining their scope, purpose, applicability, and more.

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

NLU goes beyond surface-level analysis and attempts to comprehend the contextual meanings, intents, and emotions behind the language. Because they both deal with Natural Language, these names are sometimes interchangeable. Natural language processing can take on a variety of forms, but all are generally driven by two subsets of NLP that have similar names, sometimes used interchangeably.

While people can identify homographs from the context of a sentence, an AI model lacks this contextual understanding. Traditional surveys force employees to fit their answer into a multiple-choice box, even when it doesn’t. Using the power of artificial intelligence and NLU technology, companies can create surveys full of open-ended questions.

For instance, a simple chatbot can be developed using NLP without the need for NLU. However, for a more intelligent and contextually-aware assistant capable of sophisticated, natural-sounding conversations, natural language understanding becomes essential. It enables the assistant to grasp the intent behind each user utterance, ensuring proper understanding and appropriate responses. Across various industries and applications, NLP and NLU showcase their unique capabilities in transforming the way we interact with machines. By understanding their distinct strengths and limitations, businesses can leverage these technologies to streamline processes, enhance customer experiences, and unlock new opportunities for growth and innovation. NLU focuses on understanding the meaning and intent of human language, while NLP encompasses a broader range of language processing tasks, including translation, summarization, and text generation.

Natural language generation (NLG) is a process within natural language processing that deals with creating text from data. Summarizing documents and generating reports is yet another example of an impressive use case for AI. We can generate
reports on the fly using natural language processing tools trained in parsing and generating coherent text documents. Since the program always tries to find a content-wise synonym to complete the task, the results are much more accurate
and meaningful.

Natural Language Processing Tasks:

As you can see, setting up your own NLP chatbots is relatively easy if you allow a chatbot service to do all the heavy lifting for you. And in case you need more help, you can always reach out to the Tidio team or read our detailed guide on how to build a chatbot from scratch. Last but not least, Tidio provides comprehensive analytics to help you monitor your chatbot’s performance and customer satisfaction. For instance, you can see the engagement rates, how many users found the chatbot helpful, or how many queries your bot couldn’t answer. To design the bot conversation flows and chatbot behavior, you’ll need to create a diagram. It will show how the chatbot should respond to different user inputs and actions.

• Google leverages the power of Natural Language Processing (NLP) to understand and process user intent.
• For example, in NLU, various ML algorithms are used to identify the sentiment, perform Name Entity Recognition (NER), process semantics, etc.
• NLU can analyze the sentiment or emotion expressed in text, determining whether the sentiment is positive, negative, or neutral.

Sentence breaking is done manually by humans, and then the sentence pieces are put back together again to form one
coherent text. Sentences are broken on punctuation marks, commas in lists, conjunctions like “and”
or “or” etc. It also needs to consider other sentence specifics, like that not every period ends a sentence (e.g., like
the period in “Dr.”). Once you’ve identified trends — across all of the different channels — you can use these insights to make informed decisions on how to improve customer satisfaction.

As a result, NLU  deals with more advanced tasks like semantic analysis, coreference resolution, and intent recognition. While natural language understanding focuses on computer reading comprehension, natural language generation enables computers to write. NLG is the process of producing a human language text response based on some data input. This text can also be converted into a speech format through text-to-speech services.

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