Improving large-scale k-nearest neighbor text categorization with label autoencoders
FECHA:
2022-08-11
IDENTIFICADOR UNIVERSAL: http://hdl.handle.net/11093/3804
VERSIÓN EDITADA: https://www.mdpi.com/2227-7390/10/16/2867
TIPO DE DOCUMENTO: article
RESUMEN
In this paper, we introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections in the presence of complex and structured label vocabularies with high inter-label correlation. The proposed method is an evolution of the traditional k-Nearest Neighbors algorithm which uses a large autoencoder trained to map the large label space to a reduced size latent space and to regenerate the predicted labels from this latent space. We have evaluated our proposal in a large portion of the MEDLINE biomedical document collection which uses the Medical Subject Headings (MeSH) thesaurus as a controlled vocabulary. In our experiments we propose and evaluate several document representation approaches and different label autoencoder configurations.