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.
@article{arxiv.2402.01963,
title = {Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders},
author = {Francisco J. Ribadas-Pena and Shuyuan Cao and Víctor M. Darriba Bilbao},
journal= {arXiv preprint arXiv:2402.01963},
year = {2024}
}