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Leveraging knowledge graphs to update scientific word embeddings using latent semantic imputation. (arXiv:2210.15358v1 [cs.CL])
cs.CL updates on arXiv.org arxiv.org
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 …
arxiv graphs imputation knowledge knowledge graphs semantic word embeddings