Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation
This addresses the problem of embedding rare scientific words for researchers in biomedical fields, but it is incremental as it builds on existing embedding methods with a specific adaptation.
The paper tackled the challenge of generating quality word embeddings for rare or novel scientific terms by using latent semantic imputation (LSI) with knowledge graphs, resulting in reliable embeddings for rare and out-of-vocabulary terms in the biomedical domain as evaluated on a domain-specific similarity task.
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.