What is Natural Language Understanding NLU and how is it used in practice?
NLU algorithms often operate on text that has already been standardized by text pre-processing steps. 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). 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.
Conversational interfaces, also known as chatbots, sit on the front end of a website in order for customers to interact with a business. Because conversational interfaces are designed to emulate “human-like” conversation, natural language understanding and natural language processing play a large part in making the systems capable of doing their jobs. Whether you’re on your computer all day or visiting a company page seeking support via a chatbot, it’s likely you’ve interacted with a form of natural language understanding. When it comes to customer support, companies utilize NLU in artificially intelligent chatbots and assistants, so that they can triage customer tickets as well as understand customer feedback. Forethought’s own customer support AI uses NLU as part of its comprehension process before categorizing tickets, as well as suggesting answers to customer concerns.
NLP vs NLU vs NLG for Contact Centers
For example, the meaning of a simple word like “premium” is context-specific depending on the nature of the business a customer is interacting with. Now that we understand the basics of NLP, NLU, and NLG, let’s take a closer look at the key components of each technology. These components are the building blocks that work together to enable chatbots to understand, interpret, and generate natural language data. By leveraging these technologies, chatbots can provide efficient and effective customer service and support, freeing up human agents to focus on more complex tasks.
Let us know more about them in-depth and learn about each technology and its application in the blog. Virtual assistants and chatbots have become an integral part of our lives, and that’s where NLU and NLP truly shine. By recognizing the goals and techniques employed in each field, we can harness their power more effectively and explore innovative solutions to language-related challenges. “The output of NLP can be used for subsequent processing or search,” the company explains. For example, early optical character recognition systems relied on specialized fonts that computers could detect. The experience of using a smartphone, for example, wouldn’t be quite the same without the ability to pull up a map with a computerized voice navigating your next turn.
Understanding Chatbot AI: NLP vs. NLU vs. NLG
The Global AI Adoption Index 2021, an IBM Watson project, found that nearly half of businesses are using some form of NLP technology, with another quarter of businesses expected to use it within the next 12 months. Manual ticketing is a tedious, inefficient process that often leads to delays, frustration, and miscommunication. This technology allows your system to understand the text within each ticket, effectively filtering and routing tasks to the appropriate expert or department.
Perhaps the easiest way to answer the question, “What is natural language understanding? ” is by exploring some examples of how this process shows up in the technology and tools we use every day. Systems must constantly work to better understand language by taking in information from a wide range of sources. Here is a breakdown of the steps involved in natural language understanding and the roles each of them plays. NLG is used in a variety of applications, including chatbots, virtual assistants, and content creation tools.
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Read more about What is the difference between NLP and Use Cases here.