Applying Machine Learning and Natural Language Processing Methods to Support Taxonomy Development and Maintenance
研究了如何用机器学习和大语言模型自动构建分类法，通过评估多种嵌入模型、聚类算法和生成模型，发现该方法能高效生成与已有分类法结构相似的分类体系。
Taxonomies provide a systematic way to organize phenomena and have various practical and theoretical benefits for organizational researchers and practitioners. While taxonomy development and maintenance is often a burdensome process (e.g., time-consuming, costly, and prone to judgmental error), advances in natural language processing (NLP) have the potential to streamline this process. In this study, we employed various evaluation metrics (e.g., cosine similarity) to investigate how machine learning (ML) methods and large language models (LLMs) can automate taxonomy development and maintenance. We examined two embedding models, six clustering algorithms, and three generative LLMs (for creating cluster labels) to construct taxonomies and compared their alignment with four established taxonomies (CABIN, IPIP-NEO-120, ATAF, and O*NET). The confirmatory taxonomic method we examined resulted in effective clustering (i.e., similar text statements were consistently grouped), frequently yielded structures similar to the original taxonomies for ATAF, IPIP-NEO-120, and CABIN (with O*NET being more variable), and resulted in extremely efficient taxonomy title generation. These findings can provide researchers with a foundation for how to approach NLP-based taxonomy development and maintenance activities for their own contexts.