Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation

• URL: http://arxiv.org/abs/2210.15358v1
• Date: Thu, 27 Oct 2022 12:15:26 GMT
• Title: Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation
• Authors: Jason Hoelscher-Obermaier, Edward Stevinson, Valentin Stauber, Ivaylo Zhelev, Victor Botev, Ronin Wu, Jeremy Minton
• Abstract summary: We show how glslsi can impute embeddings for domain-specific words from up-to-date knowledge graphs. We show that LSI can produce reliable embedding vectors for rare and OOV terms in the biomedical domain.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The most interesting words in scientific texts will often be novel or rare. This presents a challenge for scientific word embedding models to determine quality embedding vectors for useful terms that are infrequent or newly emerging. We demonstrate how \gls{lsi} can address this problem by imputing embeddings for domain-specific words from up-to-date knowledge graphs while otherwise preserving the original word embedding model. We use the MeSH knowledge graph to impute embedding vectors for biomedical terminology without retraining and evaluate the resulting embedding model on a domain-specific word-pair similarity task. We show that LSI can produce reliable embedding vectors for rare and OOV terms in the biomedical domain.

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