What Is Natural Language Understanding NLU?

If you produce templated content regularly, say a story based on the Labor Department’s quarterly jobs report, you can use NLG to analyze the data and write a basic narrative based on the numbers. It takes data from a search result, for example, and turns it into understandable language. So whenever you ask your smart device, “What’s it like on I-93 right now? Once a chatbot, smart device, or search function understands the language it’s “hearing,” it has to talk back to you in a way that you, in turn, will understand. NLP is also used whenever you ask Alexa, Siri, Google, or Cortana a question, and anytime you use a chatbot. The program is analyzing your language against thousands of other similar queries to give you the best search results or answer to your question.

Hiren is VP of Technology at Simform with an extensive experience in helping enterprises and startups streamline their business performance through data-driven innovation. These capabilities, and more, allow developers to experiment with NLU and build pipelines for their specific use cases to customize their text, audio, and video data further. Considering the complexity of language, creating a tool that bypasses significant limitations such as interpretations and context can be ambitious and demanding. Because of its immense influence on our economy and everyday lives, it’s incredibly important to understand key aspects of AI, and potentially even implement them into our business practices. Here is a benchmark article by SnipsAI, AI voice platform, comparing F1-scores, a measure of accuracy, of different conversational AI providers. This is achieved by the training and continuous learning capabilities of the NLU solution.

NLP vs NLU vs. NLG summary

But while playing chess isn’t inherently easier than processing language, chess does have extremely well-defined rules. There are certain moves each piece can make and only a certain amount of space on the board for them to move. Computers thrive at finding patterns when provided with this kind of rigid structure. Most of the time financial consultants try to understand what customers were looking for since customers do not use the technical lingo of investment. Since customers’ input is not standardized, chatbots need powerful NLU capabilities to understand customers.

Therefore, their predicting abilities improve as they are exposed to more data. By considering clients’ habits and hobbies, nowadays chatbots recommend holiday packages to customers (see Figure 8). Since it is not a standardized conversation, NLU capabilities are required. False patient reviews can hurt both businesses and those seeking treatment. Sentiment analysis, thus NLU, can locate fraudulent reviews by identifying the text’s emotional character.

Semantic analysis

Learn how they differ and why they are important for your AI initiatives. A number of advanced NLU techniques use the structured information provided by NLP to understand a given user’s intent. ATNs and their more general format called “generalized ATNs” continued to be used for a number of years. NLU, the technology behind nlu and nlp intent recognition, enables companies to build efficient chatbots. In order to help corporate executives raise the possibility that their chatbot investments will be successful, we address NLU-related questions in this article. With FAQ chatbots, businesses can reduce their customer care workload (see Figure 5).