Creating a Fine Grained Entity Type Taxonomy Using LLMs
Michael Gunn Dohyun Park Nidhish Kamath
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.