An Intelligent Chatbot System Based on Entity Extraction Using RASA NLU and Neural Network

Abstract Intelligent chatbot systems are popular issues in the application fields of robot system and natural language processing. As the development of natural language processing and neural network algorithms, the application of artificial intelligence is increasing in Chatbot systems, which are typically used in dialog systems for various practical purposes including customer service or information acquisition. This paper designs the functional framework and introduces the principle of RASA NLU for the Chatbot system, then it integrates RASA NLU and neural network (NN) methods and implements the system based on entity extraction after intent recognition. With the experimental comparison and validation, our developed system can realize automatic learning and answering the collected questions about finance. The system analysis of two methods also validate that RASA NLU outperforms NN in accuracy for a single experiment, but NN has better integrity to classify entities from segmented words.