Jeremy Minton


2022

pdf bib
Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation
Jason Hoelscher-Obermaier | Edward Stevinson | Valentin Stauber | Ivaylo Zhelev | Viktor Botev | Ronin Wu | Jeremy Minton
Proceedings of the first Workshop on Information Extraction from Scientific Publications

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 Latent Semantic Imputation (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 out-of-vocabulary terms in the biomedical domain.