EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain

Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, Guergana Savova


Abstract
Transformer-based neural language models have led to breakthroughs for a variety of natural language processing (NLP) tasks. However, most models are pretrained on general domain data. We propose a methodology to produce a model focused on the clinical domain: continued pretraining of a model with a broad representation of biomedical terminology (PubMedBERT) on a clinical corpus along with a novel entity-centric masking strategy to infuse domain knowledge in the learning process. We show that such a model achieves superior results on clinical extraction tasks by comparing our entity-centric masking strategy with classic random masking on three clinical NLP tasks: cross-domain negation detection, document time relation (DocTimeRel) classification, and temporal relation extraction. We also evaluate our models on the PubMedQA dataset to measure the models’ performance on a non-entity-centric task in the biomedical domain. The language addressed in this work is English.
Anthology ID:
2021.bionlp-1.21
Volume:
Proceedings of the 20th Workshop on Biomedical Language Processing
Month:
June
Year:
2021
Address:
Online
Venues:
BioNLP | NAACL
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
191–201
Language:
URL:
https://aclanthology.org/2021.bionlp-1.21
DOI:
10.18653/v1/2021.bionlp-1.21
Bibkey:
Cite (ACL):
Chen Lin, Timothy Miller, Dmitriy Dligach, Steven Bethard, and Guergana Savova. 2021. EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain. In Proceedings of the 20th Workshop on Biomedical Language Processing, pages 191–201, Online. Association for Computational Linguistics.
Cite (Informal):
EntityBERT: Entity-centric Masking Strategy for Model Pretraining for the Clinical Domain (Lin et al., BioNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.bionlp-1.21.pdf
Data
PubMedQA