Chapter 2: Natural Language Understanding in Rasa

In this chapter, we introduce how to implement Natural Language Understanding (NLU) in Rasa.

Rasa NLU is responsible for intent recognition and entity extraction. For example, if the user input is What's the weather like tomorrow in New York?, Rasa NLU needs to extract that the intent of the user is asking for weather, and the corresponding entity names and type, for example, the date is tomorrow, and the location is New York.

Rasa NLU uses supervised learning algorithms to fulfill this function. A proper number of examples including intent and entity information are needed for training the NLU model. Rasa NLU has a very flexible software architecture design and supports various kinds of algorithms. The implementations of those algorithms are called components. Components also need to be carefully configured and maintain a correct dependency relationship between their upstream and downstream components. Rasa NLU introduces...