In overview, the RASA framework's interpreter turns the input message into a dictionary that includes the original text, the intent, and any entities discovered which is then sent to the rasa core (Dialogue management) for further process. Here in this sentence, we extracted intent as request weather and entity as tomorrow. The entity extraction is depending on users' data if they have entities in it. Here is the result in JSON to markdown format to have

So, after we establish intent and entities, let us see Let us figure out one thing by looking at this sentence:ДЄnd we can observe that the intention of this sentence is Let us investigate one example to understand the process clearly. Rasa NLU interprets the user message and extracts intent and entities using the help of various pipelines. So, the Rasa build a tool called Rasa NLU which is an open-source natural language processing tool that can be used in chatbots to classify intent, extract entities, and sentiment analysis. Natural language understanding and natural language generation are the subtopics of natural language processing which handles the unstructured user language and convert it into structured machine understanding language to have the interaction between them.

Natural language understanding is a topic which deals with an interaction between humans and computers. It is transparent and customizable in terms of development. Rasa is an effective and time-efficient tool to build complex chatbots and works out of the box in dialogue management. Rasa core takes the user's input and generates a response accordingly using various pipelines. Rasa NLU examines the user's input, classifies the intent, and extracts the entities. Rasa NLU and Rasa Core are the two modules that make up RASA.

The Dual Intent and Entity Transformer (DIET) model for natural language processing (NLP) is implemented in RASA, which is an open-source implementation.

Rasa chatbot