부산대학교 도서관

Natural Language Processing (NLP) aims to study various theories and methods that enable effective communication between humans and computers in natural language. One specific technique, known as Keyphrase Extraction (KPE), has achieved significant success in recent years through pre-trained Language Models (LM), particularly BERT and ELMo. Currently, researchers have presented NLP4RE at Requirements Engineering (RE) conferences, contemplating how to leverage the cutting-edge advancements in NLP to achieve the integration of closely related research domains. In the practice of requirements engineering, it is not always assumed that the initial requirements description is complete, which can lead to requirements missing or changes. To address this issue, this paper proposes an unsupervised keyword extraction modeling method. Specifically, using the problem frames model as a case study, this method is integrated into the team's development of an iOS-based Problem Frames (PF) modeling tool in the form of an assisting dropdown list. It is linked with external knowledge bases to predict new keywords. We compare five unsupervised keyword extraction techniques with different principles and evaluate them using samples from the requirements engineering domain. In addition, an eye movement experiment is conducted to further assess the proposed method.