Improving Large-Scale k -Nearest Neighbor Text Categorization with Label Autoencoders

Francisco J. Ribadas-Pena (), Shuyuan Cao and Víctor M. Darriba Bilbao
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Francisco J. Ribadas-Pena: Department of Computer Science, University of Vigo, Edificio Politécnico, Campus As Lagoas s/n, 32004 Ourense, Spain
Shuyuan Cao: Department of Computer Science, University of Vigo, Edificio Politécnico, Campus As Lagoas s/n, 32004 Ourense, Spain
Víctor M. Darriba Bilbao: Department of Computer Science, University of Vigo, Edificio Politécnico, Campus As Lagoas s/n, 32004 Ourense, Spain

Mathematics, 2022, vol. 10, issue 16, 1-22

Abstract: 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.

Keywords: autoencoders; multi-label categorization; semantic indexing; nearest neighbors; text categorization; MeSH indexing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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