What is NLU Natural Language Understanding?

how does nlu work

However, NLG technology makes it possible for computers to produce humanlike text that emulates human writers. This process starts by identifying a document’s main topic and then leverages NLP to figure out how the document should be written in the user’s native language. The algorithms utilized in NLG play a vital role in ensuring the generation of coherent and meaningful language.

how does nlu work

Preprocessing includes noise removal, tokenization, and word normalization. Next, the sentiment analysis model labels each sentence or paragraph based on its sentiment polarity. Akkio is used to build NLU models for computational linguistics tasks like machine translation, question answering, and social media analysis. With Akkio, you can develop NLU models and deploy them into production for real-time predictions. Natural Language Understanding (NLU) models are used to interpret and analyze text data in order to identify meaning and intent.

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It takes into account the broader context and prior knowledge to comprehend the meaning behind the ambiguous or indirect language. NLU seeks to identify the underlying intent or purpose behind a given piece of text or speech. It classifies the user’s intention, whether it is a request for information, a command, a question, or an expression of sentiment.

how does nlu work

Instead, we use a mixture of LSTM (Long-Short-Term-Memory), GRU (Gated Recurrent Units) and CNN (Convolutional Neural Networks). In simpler terms; a deep learning model will be able to perceive and understand the nuances of human language. Parsing is merely a small aspect of natural language understanding in AI – other, more complex tasks include semantic role labelling, entity recognition, and sentiment analysis.

Tools to implement NLU

With elfoBOT’s solution, you can use our chatbot platform to build AI chatbots to keep your customers engaged in meaningful ways. If NLP is about understanding the state of the game, NLU is about strategically applying that information to win the game. Thinking dozens of moves ahead is only possible after determining the ground rules and the context. Working together, these two techniques are what makes a conversational AI system a reality. Consider the requests in Figure 3 — NLP’s previous work breaking down utterances into parts, separating the noise, and correcting the typos enable NLU to exactly determine what the users need. Named Entity Recognition is the process of recognizing “named entities”, which are people, and important places/things.


Chatbots created through Botpress may be able to grasp concepts with as few as 10 examples of an intent, directly impacting the speed at which a chatbot is ready to engage real humans. Training data organizes unstructured language into sets known as “buckets”. The purpose of these buckets is to contain examples of speech that, although different, have the same or similar meaning. For instance, the same bucket may contain the phrases “book me a ride” and “Please, call a taxi to my location”, as the intent of both phrases alludes to the same action. Depending on your business, you may need to process data in a number of languages. Having support for many languages other than English will help you be more effective at meeting customer expectations.

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John Ball, cognitive scientist and inventor of Patom Theory, supports this assessment. Natural language processing has made inroads for applications to support human productivity in service and ecommerce, but this has largely been made possible by narrowing the scope of the application. There are thousands of ways to request something in a human language that still defies conventional natural language processing. NLU is the technology behind chatbots, computer programs that communicate with humans in natural language via text or voice. Chatbots can follow a script and answer, only the questions in that script.

how does nlu work

NLP focuses on processing and analyzing text data, such as language translation or speech recognition. NLU goes a step further by understanding the context and meaning behind the text data, allowing for more advanced applications such as chatbots or virtual assistants. NLU is technically a sub-area of the broader area of natural language processing (NLP), which is a sub-area of artificial intelligence (AI). Many NLP tasks, such as part-of-speech or text categorization, do not always require actual understanding in order to perform accurately, but in some cases they might, which leads to confusion between these two terms. As a rule of thumb, an algorithm that builds a model that understands meaning falls under natural language understanding, not just natural language processing.

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how does nlu work

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