In standard methodology for natural language processing, entities in text are typically embedded in dense vector spaces with pre-trained models. Such approaches are strong building blocks for entity-related tasks, but the embeddings they produce require extensive additional processing in neural models, and these entity embeddings are fundamentally difficult to interpret. In this paper, we present an approach to creating interpretable 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  achieve competitive performance with ELMo and BERT without using any extra parameters . 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 simple rules to incorporate domain knowledge and improve performance.
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.
R
Such approaches are strong building blocks for entity-related tasks, but the embeddings they produce require extensive additional processing in neural models, and these entity embeddings
The embeddings produced this way are effective when fed into downstream models, but they require end-task fine-tuning and
D
interpretable
A
used in parameter-free downstream models
R
ELMo and BERT without using any extra parameters
ELMo- and BERT-based embeddings in trained models
R
simple
a small number of