In standard methodology for natural language processing, entities in text are
typically embedded in dense vector spaces with pre-trained models. The
embeddings produced this way are effective when fed into downstream models, but
they require end-task fine-tuning and are fundamentally difficult to interpret.
In this paper, we present an approach to creating entity representations that
are human readable and achieve high performance on entity-related tasks out of
the box. Our representations are vectors whose values correspond to posterior
probabilities over fine-grained entity types, indicating the confidence of a
typing model's decision that the entity belongs to the corresponding type. We
obtain these representations using a fine-grained entity typing model, trained
either on supervised ultra-fine entity typing data (Choi et al. 2018) or
distantly-supervised examples from Wikipedia. On entity probing tasks involving
recognizing entity identity, our embeddings used in parameter-free downstream
models achieve competitive performance with ELMo- and BERT-based embeddings in
trained models. We also show that it is possible to reduce the size of our type
set in a learning-based way for particular domains. Finally, we show that these
embeddings can be post-hoc modified through a small number of rules to
incorporate domain knowledge and improve performance.

本研究提出了一种利用细粒度实体类型推断模型生成具备人类可读性，且能够直接用于实体相关任务的实体表示向量的方法，并针对特定领域实体优化表征，通过少量规则增加领域知识以提高性能。