Natural Language Understanding

What is Natural Language Understanding?

Natural Language Understanding (NLU) refers to techniques and methods for understanding natural language by machine. This enables machines to understand the meaning of individual words and entire sentences. Applications are chatbots that can communicate with users in natural language.

With NLU, texts can be fully understood and interpreted through the use of artificial intelligence. This means that work processes, for example, can be made significantly more efficient.

What are the fields of application for Natural Language Understanding?

E-mails can be classified in order to create a Filtering communication within the company. In addition, NLU can be used for chatbots to recognise users' concerns and generate targeted outputs. On the other hand, virtual voice assistants are also supported, which can understand spoken input at any linguistic level and provide users with a response.

How can NLU systems be classified in artificial intelligence?

NLU systems are much more than simple speech recognition systems. They understand semantic relationships and can gauge the meaning and correctly classify the context of the statements and draw appropriate conclusions. NLU systems are used in the immediate Human-Computer Interaction used and are a kind of precursor to artificial intelligence. They can issue non-formalised commands to each other in human language and thus people can communicate with computers just as if they were humans. The understanding can include sentences that are not pronounced correctly as well as statements spoken in dialect. It is even possible that instructions or statements that are not understood can be clarified by asking the NLU system.

How are NLP and NLU related?

Natural Language Processing (NLP) is superior to NLU. NLU should be able to recognise intentions and resolve ambiguities in the context and the words. It should be able to generate well-formed speech utterances independently in a very simple way. The NLU algorithms should be able to deal with the extraordinarily complex problems of semantic interpretation and be able to recognise the intended meaning of spoken or written language. Also subtleties and contextual interpretations and Conclusions should be understood as in a human being.

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