Replacing non-biomedical concepts improves embedding of biomedical concepts-Reference-Cited by-同舟云学术

Replacing non-biomedical concepts improves embedding of biomedical concepts

Published:2024-07-04 Issue: Volume: Page:
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Author:

Niyonkuru Enock^ORCID,Soto Gomez Mauricio^ORCID,Casiraghi Elena^ORCID,Antogiovanni Stephan,Blau Hannah^ORCID,Reese Justin T^ORCID,Valentini Giorgio^ORCID,Robinson Peter N^ORCID

Abstract

AbstractObjectivesConcept embeddings are low-dimensional vector representations of concepts such as MeSH:D009203 (Myocardial Infarction), whose similarity in the embedded vector space reflects their semantic similarity. Here, we test the hypothesis that non-biomedical concept synonym replacement can improve the quality of biomedical concepts embeddings.Materials and methodsWe developed an approach that leverages WordNet to replace sets of synonyms with the most common representative of the synonym set.ResultsWe tested our approach on 1055 concept sets and found that, on average, the mean intracluster distance was reduced by 8% in the vector-space. Assuming that homophily of related concepts in the vector space is desirable, our approach tends to improve the quality of embeddings.Discussion and ConclusionThis pilot study shows that non-biomedical synonym replacement tends to improve the quality of embeddings of biomedical concepts using the Word2Vec algorithm. We have implemented our approach in a freely available Python package available athttps://github.com/TheJacksonLaboratory/wn2vec.

Publisher

Cold Spring Harbor Laboratory

Reference18 articles.

1. Tomas Mikolov , Kai Chen , Greg Corrado , and Jeffrey Dean . Efficient estimation of word representations in vector space. In Proceedings of the First International Conference on Learning Representations, 2013.

2. Yoav Goldberg and Omer Levy . word2vec explained: deriving Mikolov et al.’s negative-sampling word-embedding method, 2014. cite arxiv:1402.3722.

3. Jeffrey Pennington , Richard Socher , and Christopher Manning . GloVe: Global vectors for word representation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1532–1543, Doha, Qatar, October 2014. Association for Computational Linguistics.

4. Shuigeng Zhou , Tok Wang Ling , Jihong Guan , Jiangtao Hu , and Aoying Zhou . Fast text classification: a training-corpus pruning based approach. In Proceedings of the Eighth International Conference on Database Systems for Advanced Applications, 2003. (DASFAA 2003), pages 127–136. IEEE, 2003.

5. Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, xMinneapolis, Minnesota, June 2019. Association for Computational Linguistics.