Biomedical Semantic Indexing using Dense Word Vectors in BioASQ

Background: Biomedical curators are often required to semantically index large numbers of biomedical articles, using hierarchically related labels (e.g., MeSH headings). Large scale hierarchical classification, a branch of machine learning, can facilitate this procedure, but the resulting automatic classifiers are often inefficient because of the very large dimensionality of the dominant bag-of-words representation of texts. Feature selection quickly harms the accuracy of the classifiers in this particular task, and dimensionality reduction transformations (e.g., PCA-based) usually cannot be efficiently applied to very large corpora.