BUILDING AN ADMISSION QUESTION & ANSWERING SYSTEM FOR CAN THO UNIVERSITY OF TECHNOLOGY

Tóm tắt

The problem of developing a question-and-answering system is a challenging task in the field of Natural Language Processing. Natural language is inherently ambiguous, and determining the semantic meaning of questions as well as identifying the correct answers poses significant challenges. In this research, we investigate a question-and-answering system based on the Rasa Framework. The system’s model is capable of memorizing and accurately answering questions it has encountered during the training phase. Additionally, the model can handle new questions during the testing phase by selecting an appropriate answer from the set of answers provided during training. The dataset used for training the model consists of 720 questions and 136 intents, collected from the admission information of Can Tho University of Technology, students, and the Internet up to 2024. The answers to the questions were gathered from the university's admission experts. Based on the results, we propose a question-and-answering process and develop a web-based admission question-and-answering system to replace advisors in responding to user inquiries online regarding the university's admission information. The system's experimental results demonstrate that the natural language understanding model achieves an accuracy of 97.4% on the test set and an expert-evaluated accuracy of 90.1%.