Biomedical Semantic Indexing using Dense Word Vectors in BioASQ

available at the end of the article 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. Methods: We examine the use of dense word vectors, also known as word embeddings, as an efficient method of dimensionality reduction that makes hierarchical text classification algorithms more scalable in biomedical semantic indexing, withou