Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus

TL;DR

This study evaluates whether biomedical BERT models improve vocabulary alignment in the UMLS Metathesaurus and finds that traditional BioWordVec embeddings outperform BERT-based approaches for synonymy prediction.

Contribution

The paper systematically compares biomedical BERT models with existing methods for UMLS synonymy prediction, revealing BERT's limitations in this specific task.

Findings

BERT-based models do not outperform BioWordVec with Siamese Networks.

Original BioBERT outperforms SapBERT models pre-trained with UMLS.

Siamese Networks yield better results than BERT models for synonym prediction.

Abstract

The current UMLS (Unified Medical Language System) Metathesaurus construction process for integrating over 200 biomedical source vocabularies is expensive and error-prone as it relies on the lexical algorithms and human editors for deciding if the two biomedical terms are synonymous. Recent advances in Natural Language Processing such as Transformer models like BERT and its biomedical variants with contextualized word embeddings have achieved state-of-the-art (SOTA) performance on downstream tasks. We aim to validate if these approaches using the BERT models can actually outperform the existing approaches for predicting synonymy in the UMLS Metathesaurus. In the existing Siamese Networks with LSTM and BioWordVec embeddings, we replace the BioWordVec embeddings with the biomedical BERT embeddings extracted from each BERT model using different ways of extraction. In the Transformer architecture, we evaluate the use of the different biomedical BERT models that have been pre-trained using different datasets and tasks. Given the SOTA performance of these BERT models for other downstream tasks, our experiments yield surprisingly interesting results: (1) in both model architectures, the approaches employing these biomedical BERT-based models do not outperform the existing approaches using Siamese Network with BioWordVec embeddings for the UMLS synonymy prediction task, (2) the original BioBERT large model that has not been pre-trained with the UMLS outperforms the SapBERT models that have been pre-trained with the UMLS, and (3) using the Siamese Networks yields better performance for synonymy prediction when compared to using the biomedical BERT models.