OntoType: Ontology-Guided Zero-Shot Fine-Grained Entity Typing with Weak
  Supervision from Pre-Trained Language Models
Tanay Komarlu Minhao Jiang Xuan Wang Jiawei Han
Fine-grained entity typing (FET), which assigns entities in text with
context-sensitive, fine-grained semantic types, will play an important role in
natural language understanding. A supervised FET method, which typically relies
on human-annotated corpora for training, is costly and difficult to scale.
Recent studies leverage pre-trained language models (PLMs) to generate rich and
context-aware weak supervision for FET. However, a PLM may still generate a
mixture of rough and fine-grained types, or tokens unsuitable for typing. In
this study, we vision that an ontology provides a semantics-rich, hierarchical
structure, which will help select the best results generated by multiple PLM
models and head words. Specifically, we propose a novel zero-shot,
ontology-guided FET method, OntoType, which follows a type ontological
structure, from coarse to fine, ensembles multiple PLM prompting results to
generate a set of type candidates, and refines its type resolution, under the
local context with a natural language inference model. Our experiments on the
Ontonotes, FIGER, and NYT datasets using their associated ontological
structures demonstrate that our method outperforms the state-of-the-art
zero-shot fine-grained entity typing methods. Our error analysis shows that
refinement of the existing ontology structures will further improve
fine-grained entity typing.