In this study, we investigate the potential of GPT-4 and its advanced
iteration, GPT-4 Turbo, in autonomously developing a detailed entity type
taxonomy. Our objective is to construct a comprehensive taxonomy, starting from
a broad classification of entity types - including objects, time, locations,
organizations, events, actions, and subjects - similar to existing manually
curated taxonomies. This classification is then progressively refined through
iterative prompting techniques, leveraging GPT-4's internal knowledge base. The
result is an extensive taxonomy comprising over 5000 nuanced entity types,
which demonstrates remarkable quality upon subjective evaluation.
We employed a straightforward yet effective prompting strategy, enabling the
taxonomy to be dynamically expanded. The practical applications of this
detailed taxonomy are diverse and significant. It facilitates the creation of
new, more intricate branches through pattern-based combinations and notably
enhances information extraction tasks, such as relation extraction and event
argument extraction. Our methodology not only introduces an innovative approach
to taxonomy creation but also opens new avenues for applying such taxonomies in
various computational linguistics and AI-related fields.

通过使用 GPT-4 和其高级版本 GPT-4 Turbo，本研究探讨了它们在自主开发详细实体类型分类系统方面的潜力。我们的目标是构建一个全面的分类系统，从广泛的实体类型分类开始，包括对象、时间、地点、组织、事件、行动和主题等，类似于现有的手动定义的分类系统。通过迭代的提示技术，利用 GPT-4 内部的知识库，逐步完善这一分类系统。结果是一个包含超过 5000 个精细实体类型的广泛分类系统，在主观评估中表现出了卓越的质量。我们采用了一种简单而有效的提示策略，使分类系统能够动态扩展。这个详细分类系统的实际应用非常广泛且重要。它通过基于模式的组合，有助于创建新的复杂分支，显著提升信息提取任务，如关系提取和事件参数提取。我们的方法不仅为分类系统的创建引入了创新性方法，还为在各种计算语言学和人工智能相关领域应用该分类系统开辟了新的途径。