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

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 Nei...

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Bibliographic Details
Published in:	arXiv.org
Main Authors:	Ribadas-Pena, Francisco J, Cao, Shuyuan, Víctor M Darriba Bilbao
Format:	Paper
Language:	English
Published:	Ithaca Cornell University Library, arXiv.org 03.02.2024
Subjects:	Algorithms Documents Labels
ISSN:	2331-8422
Online Access:	Get full text
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Summary:	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.
Bibliography:	SourceType-Working Papers-1 ObjectType-Working Paper/Pre-Print-1 content type line 50
ISSN:	2331-8422
DOI:	10.48550/arxiv.2402.01963