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

• Published in: Mathematics (Basel) Vol. 10; no. 16; p. 2867
• Main Authors: Ribadas-Pena, Francisco J., Cao, Shuyuan, Darriba Bilbao, Víctor M.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2022
• Subjects: Adaptation; Algorithms; autoencoders; Classification; Data compression; Documents; Learning; Medical Subject Headings-MeSH; MeSH indexing; multi-label categorization; nearest neighbors; Neural networks; semantic indexing; Semantics; Text categorization
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10162867

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.