This paper explores in detail the technological evolution and current state of question and answer (Q&A) systems. Using an example of an airline customer service task, a BERT-based model is developed that is capable of recognising user intentions and extracting named entities. The paper provides a detailed description of the dataset preparation, data analysis methods and data exploration techniques of the project. A description of the model and parameter settings during the model tuning process and the model training process is presented. The model developed in this project is named RNEEMAviCS-BERT, which achieved an intent recognition accuracy of 98.2% and named entity recognition accuracy of 83%. We have created a semantic analysis module for the question and answer system. The next stage of our work will be to integrate the dataset to complete the query-response and response generation components of the Q&A system.

Keywords: question-answering systems, ChatGPT, BERT, machine learning, neural networks, pre-trained models, intention recognition, named entity recognition, data analysis, model training