  Pre-trained models such as BERT are widely used in NLP tasks and are
fine-tuned to improve the performance of various NLP tasks consistently.
Nevertheless, the fine-tuned BERT model trained on our protocol corpus still
has a weak performance on the Entity Linking (EL) task. In this paper, we
propose a model that joints a fine-tuned language model with an RFC Domain
Model. Firstly, we design a Protocol Knowledge Base as the guideline for
protocol EL. Secondly, we propose a novel model, PEL-BERT, to link named
entities in protocols to categories in Protocol Knowledge Base. Finally, we
conduct a comprehensive study on the performance of pre-trained language models
on descriptive texts and abstract concepts. Experimental results demonstrate
that our model achieves state-of-the-art performance in EL on our annotated
dataset, outperforming all the baselines.
