NLP vs NLU vs NLG: Understanding the Differences by Tathagata Medium

It plays an important role in customer service and virtual assistants, allowing computers to understand text in the same way humans do. By using NLU technology, businesses can automate their content analysis and intent recognition processes, saving time and resources. It can also provide actionable data insights that lead to informed decision-making. Techniques commonly used in NLU include deep learning and statistical machine translation, which allows for more accurate and real-time analysis of text data. Overall, NLU technology is set to revolutionize the way businesses handle text data and provide a more personalized and efficient customer experience. Ultimately, we can say that natural language understanding works by employing algorithms and machine learning models to analyze, interpret, and understand human language through entity and intent recognition.

NLU is the broadest of the three, as it generally relates to understanding and reasoning about language. NLP is more focused on analyzing and manipulating natural language inputs, and NLG is focused on generating natural language, sometimes from scratch. Whether you’re dealing with an Intercom bot, a web search interface, or a lead-generation form, NLU can be used to understand customer intent and provide personalized responses.

Sentiment Analysis and Social Listening

Conversational AI can extrapolate which of the important words in any given sentence are most relevant to a user’s query and deliver the desired outcome with minimal confusion. Understanding the difference between these two subfields is important to develop effective and accurate language models. Questionnaires about people’s habits and health problems are insightful while making diagnoses. In this section, we will introduce the top 10 use cases, of which five are related to pure NLP capabilities and the remaining five need for NLU to assist computers in efficiently automating these use cases. Figure 4 depicts our sample of 5 use cases in which businesses should favor NLP over NLU or vice versa. Let’s illustrate this example by using a famous NLP model called Google Translate.

The “depth” is measured by the degree to which its understanding approximates that of a fluent native speaker. At the narrowest and shallowest, English-like command interpreters require minimal complexity, but have a small range of applications. Narrow but deep systems explore and model mechanisms of understanding,[24] but they still have limited application.

Conversational search: an update for 2024

Companies receive thousands of requests for support daily, so NLU algorithms are useful in prioritizing tickets and enabling support agents to handle them more efficiently. Automated reasoning is the process of using computers to reason about something. However, automated reasoning can help machines to understand human language. In the case of NLU, automated reasoning can be used to reason about the meaning of human language. Times are changing and businesses are doing everything to improve cost-efficiencies and serve their customers on their own terms. In an uncertain global economy and business landscape, one of the best ways to stay competitive is to utilise the latest, greatest, and most powerful natural language understanding AI technologies currently available.

• A lexicon for the language is required, as is some type of text parser and grammar rules to guide the creation of text representations.
• Numeric entities would be divided into number-based categories, such as quantities, dates, times, percentages and currencies.
• Akkio’s no-code AI for NLU is a comprehensive solution for understanding human language and extracting meaningful information from unstructured data.
• Robotic process automation (RPA) is an exciting software-based technology which utilises bots to automate routine tasks within applications which are meant for employee use only.

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