The MeSH-gram Neural Network Model: Extending Word Embedding Vectors with MeSH Concepts for UMLS Semantic Similarity and Relatedness in the Biomedical Domain
This work addresses semantic similarity in the biomedical domain, offering an incremental improvement over existing embedding methods.
The authors tackled the challenge of measuring semantic similarity between biomedical concepts by proposing MeSH-gram, a neural network model that extends skip-gram using MeSH descriptors, and found it outperforms skip-gram and is comparable to top methods while being more efficient.
Eliciting semantic similarity between concepts in the biomedical domain remains a challenging task. Recent approaches founded on embedding vectors have gained in popularity as they risen to efficiently capture semantic relationships The underlying idea is that two words that have close meaning gather similar contexts. In this study, we propose a new neural network model named MeSH-gram which relies on a straighforward approach that extends the skip-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 semantic similarity. MeSH-gram is first compared to skip-gram with vectors of size 300 and at several windows contexts. A deeper comparison is performed with tewenty existing models. All the obtained results of Spearman's rank correlations between human scores and computed similarities show that MeSH-gram outperforms the skip-gram model, and is comparable to the best methods but that need more computation and external resources.