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What NLP, NLU, and NLG Mean, and How They Help With Running Your Contact Center

What are NLP, NLU, and NLG, and Why should you know about them and their differences?

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

Available 24/7, chatbots and virtual assistants can speed up response times, and relieve agents from repetitive and time-consuming queries. Customer Service – NLP-powered chatbots can provide customer service by answering routine questions and handling simple requests. Converting written or spoken human speech into an acceptable and understandable for computer form are natural language processing techniques that are deemed effective and highly valuable for businesses. And because it’s created by a machine, NLG makes personalized digital engagement possible at exponentially greater scale than would be possible with human creatives—that’s a critical part of the value it delivers.

In order to respond appropriately to human language and commands, however, a computer must also use a form of data science known as natural language understanding. By looking at the ins and outs of natural language understanding (NLU), it’s possible to gain a clearer picture of the role it plays in natural language processing and artificial intelligence. In today’s age of digital communication, computers have become a vital component of our lives. As a result, understanding human language, or Natural Language Understanding (NLU), has gained immense importance. NLU is a part of artificial intelligence that allows computers to understand, interpret, and respond to human language.

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

Natural Language Processing (NLP) has witnessed groundbreaking advancements through various models, each contributing significantly to the field. These models represent pivotal moments in the journey of NLP, showcasing the evolution of how machines understand human language. Let’s explore six key NLP models that have been instrumental in this technological revolution. The overarching goal of NLP is to enrich the text in a way that it becomes more valuable. Whether it’s categorizing content, extracting information, or converting speech, NLP uses a combination of linguistic knowledge and sophisticated algorithms to process and transform language data effectively. The initial step in NLP involves breaking down the language into more minor, manageable elements.

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

NLP has existed for over 50 years and has long been used in medical research, search engines, and business intelligence platforms. While NLP can process and organize this language data in seconds, its value is limited by its inability to understand the meaning of text. NLU is a crucial part of ensuring these applications are accurate while extracting important business intelligence from customer interactions. In the near future, conversation intelligence powered by NLU will help shift the legacy contact centers to intelligence centers that deliver great customer experience. From the time we started, we have been using AI technologies like NLP, NLU & NLG to boost the contact center performance with live conversation intelligence. Our AI engine is able to uncover insights from 100% of customer interactions that maximizes frontline team performance through coaching and end-to-end workflow automation.

AI-Powered Data Stories: a new approach to data

Natural language understanding (NLU) is where you take an input text string and analyse what it means. For instance, “hello world” would be converted via NLU or natural language understanding into nouns and verbs and “I am happy” would be split into “I am” and “happy”, for the computer to understand. Natural language understanding in AI is the future because we already know that computers are capable of doing amazing things, although they still have quite a way to go in terms of understanding what people are saying.

Discover the latest trends and best practices for customer service for 2022 in the Support Academy. Customer support agents can spend hours manually routing incoming support tickets to the right agent or team, and giving each ticket a topic tag. This drives up handling times and leaves human agents with less capacity to work on more complex cases. But with advances in NLU, virtual agents are able to do this job automatically.

NLU vs. NLP: The Uncovering of AI Language Processing Secrets

According to research by McKinsey, the combination of speech data with other customer data can show the full context of a call and reveal opportunities for improvements in the customer experience. This also includes turning the  unstructured data – the plain language query –  into structured data that can be used to query the data set. Semantics tasks that use logic and linguistics to identify and establish the meaning of a text. This involves lexical semantics with which the computational meanings of a word in context are determined.

  • With an agent AI assistant, customer interactions are improved because agents have quick access to a docket of all past tickets and notes.
  • NLP, with its ability to identify and manipulate the structure of language, is indeed a powerful tool.
  • Natural language processing chatbots are used in customer service tools, virtual assistants, etc.
  • IBM Watson currently is being used to help manage an AI-driven stock index that evaluates potential investments based on in-depth analysis of data gathered on the largest publicly traded corporations.
  • NLP and NLU have unique strengths and applications as mentioned above, but their true power lies in their combined use.

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

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