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Eliciting semantic similarity between concepts in the biomedical domainremains a challenging task. Recent approaches founded on embedding vectors havegained in popularity as they risen to efficiently capture semanticrelationships The underlying idea is that two words that have close meaninggather similar contexts. In this study, we propose a new neural network modelnamed MeSH-gram which relies on a straighforward approach that extends theskip-gram neural network model by considering MeSH (Medical Subject Headings)descriptors instead words. Trained on publicly available corpus PubMed MEDLINE,MeSH-gram is evaluated on reference standards manually annotated for semanticsimilarity. MeSH-gram is first compared to skip-gram with vectors of size 300and at several windows contexts. A deeper comparison is performed with tewentyexisting models. All the obtained results of Spearman's rank correlationsbetween human scores and computed similarities show that MeSH-gram outperformsthe skip-gram model, and is comparable to the best methods but that need morecomputation and external resources.

The MeSH-gram Neural Network Model: Extending Word Embedding Vectors  with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the  Biomedical Domain