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Found 6 papers, 2 papers with code
Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesaurus Using Siamese Networks
Recent work uses a Siamese Network, initialized with BioWordVec embeddings (distributed word embeddings), for predicting synonymy among biomedical terms to automate a part of the UMLS (Unified Medical Language System) Metathesaurus construction process.
Solving the Right Problem is Key for Translational NLP: A Case Study in UMLS Vocabulary Insertion
In this work, we study the case of UMLS vocabulary insertion, an important real-world task in which hundreds of thousands of new terms, referred to as atoms, are added to the UMLS, one of the most comprehensive open-source biomedical knowledge bases.
UVA Resources for the Biomedical Vocabulary Alignment at Scale in the UMLS Metathesaurus
In this paper, we present a set of reusable and reproducible resources including (1) a dataset generator, (2) three datasets generated by using the generator, and (3) three baseline approaches.
UBERT: A Novel Language Model for Synonymy Prediction at Scale in the UMLS Metathesaurus
The effectiveness of UBERT for UMLS Metathesaurus construction process is evaluated using the UMLS Vocabulary Alignment (UVA) task.
Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus
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.
On Reasoning with RDF Statements about Statements using Singleton Property Triples
If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples?
