In today's technology-driven world, Artificial Intelligence (AI) is playing an increasingly important role in business operations. A leading enterprise software company, has been utilizing AI to improve their internal analytics system. However, their Internal Question Answering System (QAS) has been facing issues with Natural Language Processing (NLP). The NLP is responsible for understanding the user's queries and mapping the entities in them. In our research paper titled "Development and Evaluation of a Process to Train the Natural Language Processing of a Chatbot for an In-house Application," we aim to address and improve this issue.

Our main research question is whether the internal analytics AI can recognize and properly map all entities in a query. To answer this question, we developed a test application that includes a manual training process at the end. The training process is intended to improve the NLP, which will enable the system to better understand the user's queries. We also created a calculation concept to check the success rate of the recognized entities in a query. This validation concept will help prove the answer to our research question.

The application we developed is based on ready-made question templates, which are filled with placeholders. The placeholders are then replaced with specific business vocabulary to generate automated questions. These questions are uploaded to the NLP Repository Environment, which maps the major entities. Our process is specifically tailored to the internal reporting environment of figures, which is restricted to business vocabulary.

It's important to note that our developed process and the manual training required are only an additional solution to improve the NLP. The NLP algorithm analysis is not affected by our process.

Our research paper provides a valuable contribution to the internal analytics AI by addressing and improving the NLP in the Internal Question Answering System. By utilizing our process and training application, the system will be able to better recognize and map entities in user queries. This will result in an improved user experience and more accurate responses.