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UMLS-based data augmentation for natural language processing of clinical research literature by Tian Kang; Adler Perotte; Youlan Tang; Casey Ta; Chunhua Weng is a Health Professions article available to read on EtoBox.

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## Abstract ## Objective The study sought to develop and evaluate a knowledge-based data augmentation method to improve the performance of deep learning models for biomedical natural language processing by overcoming training data scarcity. ## Materials and Methods We extended the easy data augmentation (EDA) method for biomedical named entity recognition (NER) by incorporating the Unified Medical Language System (UMLS) knowledge and called this method UMLS-EDA. We designed experiments to systematically evaluate the effect of UMLS-EDA on popular deep learning architectures for both NER and classification. We also compared UMLS-EDA to BERT. ## Results UMLS-EDA enables substantial improvement for NER tasks from the original long short-term memory conditional random fields (LSTM-CRF) model (micro-F1 score: +5%, + 17%, and +15%), helps the LSTM-CRF model (micro-F1 score: 0.66) outperform LSTM-CRF with transfer learning by BERT (0.63), and improves the performance of the state-of-the-art sentence classification model. The largest gain on micro-F1 score is 9%, from 0.75 to 0.84, better than classifiers with BERT pretraining (0.82). ## Conclusions This study presents a UMLS-based data aug

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Author

Tian Kang; Adler Perotte; Youlan Tang; Casey Ta; Chunhua Weng

Publisher

Oxford University Press (OUP)

Published

2020

Language

EN

Field

Health Professions (Health Sciences)