Fine-grained entity typing (FET) aims to deduce specific semantic types of
the entity mentions in text. Modern methods for FET mainly focus on learning
what a certain type looks like. And few works directly model the type
differences, that is, let models know the extent that one type is different
from others. To alleviate this problem, we propose a type-enriched hierarchical
contrastive strategy for FET. Our method can directly model the differences
between hierarchical types and improve the ability to distinguish multi-grained
similar types. On the one hand, we embed type into entity contexts to make type
information directly perceptible. On the other hand, we design a constrained
contrastive strategy on the hierarchical structure to directly model the type
differences, which can simultaneously perceive the distinguishability between
types at different granularity. Experimental results on three benchmarks, BBN,
OntoNotes, and FIGER show that our method achieves significant performance on
FET by effectively modeling type differences.

该论文提出了一种类型丰富的层次对比策略用于细粒度实体类型，使模型直接区分不同层次的类型差异并提高了区分多粒度相似类型的能力。在 BBN、OntoNotes 和 FIGER 三个基准上的实验结果表明，该方法通过有效地建模类型差异在 FET 上实现了显著的性能提升。