Recently, there is an effort to extend fine-grained entity typing by using a
richer and ultra-fine set of types, and labeling noun phrases including
pronouns and nominal nouns instead of just named entity mentions. A key
challenge for this ultra-fine entity typing task is that human annotated data
are extremely scarce, and the annotation ability of existing distant or weak
supervision approaches is very limited. To remedy this problem, in this paper,
we propose to obtain training data for ultra-fine entity typing by using a BERT
Masked Language Model (MLM). Given a mention in a sentence, our approach
constructs an input for the BERT MLM so that it predicts context dependent
hypernyms of the mention, which can be used as type labels. Experimental
results demonstrate that, with the help of these automatically generated
labels, the performance of an ultra-fine entity typing model can be improved
substantially. We also show that our approach can be applied to improve
traditional fine-grained entity typing after performing simple type mapping.

本文提出了使用 BERT 遮盖语言模型 (Masked Language Model) 获得超细粒度实体类型的训练数据，并构建一个输入使其产生预测上下文相关的超类词表中的概念，其可用作类型标签。在实验结果中表明，通过这些自动生成的标签，超细粒度实体类型模型的性能大大提高。同时，我们还展示了这种方法可以用于改进传统的细粒度实体类型。