  Fine-grained entity typing (FET) is the task of identifying specific entity
types at a fine-grained level for entity mentions based on their contextual
information. Conventional methods for FET require extensive human annotation,
which is time-consuming and costly. Recent studies have been developing weakly
supervised or zero-shot approaches. We study the setting of zero-shot FET where
only an ontology is provided. However, most existing ontology structures lack
rich supporting information and even contain ambiguous relations, making them
ineffective in guiding FET. Recently developed language models, though
promising in various few-shot and zero-shot NLP tasks, may face challenges in
zero-shot FET due to their lack of interaction with task-specific ontology. In
this study, we propose OnEFET, where we (1) enrich each node in the ontology
structure with two types of extra information: instance information for
training sample augmentation and topic information to relate types to contexts,
and (2) develop a coarse-to-fine typing algorithm that exploits the enriched
information by training an entailment model with contrasting topics and
instance-based augmented training samples. Our experiments show that OnEFET
achieves high-quality fine-grained entity typing without human annotation,
outperforming existing zero-shot methods by a large margin and rivaling
supervised methods.
