Semantic indexing with deep learning: a case study

Abstract

BackgroundDeep learning techniques, particularly convolutional neural networks (CNNs), are poised for widespread application in the research fields of information retrieval and natural language processing. However, there are very few publications addressing semantic indexing with deep learning. In particular, there are few studies of semantic indexing in biomedical literature because of several specific challenges including a vast amount of semantic labels from automatically annotating MeSH terms for MEDLINE citations and a massive collection with only the title and abstract information.ResultsIn this paper, we introduce a novel CNN-based semantic indexing method for biomedical abstract document collections. First, we adaptively group word2vec categories into (coarse) subsets by clustering. Next, we construct a high-dimensional space representation with Wikipedia category extension, which contains more semantic information than bag-of-words. Thereafter, we design a hierarchical CNN indexing architecture for learning documents from a coarse- to fine-grained level with several multi-label training techniques. We believe that the low-dimensional representation of the output layer in CNNs should be more compact and effective. Finally, we perform comparative experiments for semantic indexing of biomedical abstract documents.ConclusionExperimental results on the MEDLINE dataset show that our model achieves superior performance than conventional models.