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

• URL: http://arxiv.org/abs/2402.01963v1
• Date: Sat, 3 Feb 2024 00:11:29 GMT
• Title: Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders
• Authors: Francisco J. Ribadas-Pena, Shuyuan Cao, V\'ictor M. Darriba Bilbao
• Abstract summary: We introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections. The proposed method is an evolution of the traditional k-Nearest Neighbors algorithm. We have evaluated our proposal in a large portion of the MEDLINE biomedical document collection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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