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.

本文介绍了如何使用 LSI 模型从最新的知识图谱中插入诸如罕见术语、新兴术语之类的领域特定词汇向量，以生成可靠的罕见和 OOV 术语的嵌入向量。以医学为例，使用 MeSH 知识图谱来插入生成生物医学术语嵌入向量，无需重新训练即可评估所得嵌入模型的可靠性。