Enhancing Inquisitiveness of Chatbots Through NER Integration

The need and use of virtual assistance are ever increasing. They have wide range of applications in the fields of entertainment systems, scientific research and commercial applications and can bring a drastic change in the way human-computer interaction takes place. Virtual assistance can be implemented by means of chatbot. Chatbots are software frameworks that can respond to natural language input and it converse in such a way that it imitates a real person. Chatbots are expects to give a proper response by understanding and analysing the input query. Chatbots can be used for entertainment, business and commercial purposes. Chatbot implementation can be considered successful only when it correctly analyses user query and returns an appropriate response to the user. In many of the cases, the input provided by the user is ambiguous for the chatbot to provide an accurate response. Such a situation demands for inquisitive chatbot as it becomes more interactive and communicate just like humans. This paper presents the utilization of Natural Language Processing (NLP) techniques to improve the inquisitiveness and interaction of chatbots by adapting Stanford CoreNLP framework for integrating the NLP techniques of Named Entity Recognition (NER) to chatbots.