Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911770165116928 |
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| author | Ribadas-Pena, Francisco J. Cao, Shuyuan Bilbao, Víctor M. Darriba |
| author_facet | Ribadas-Pena, Francisco J. Cao, Shuyuan Bilbao, Víctor M. Darriba |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_01963 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders Ribadas-Pena, Francisco J. Cao, Shuyuan Bilbao, Víctor M. Darriba Machine Learning Computation and Language Information Retrieval 68T50, 68T07 I.2.6; I.2.7; H.3.1 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. |
| title | Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders |
| topic | Machine Learning Computation and Language Information Retrieval 68T50, 68T07 I.2.6; I.2.7; H.3.1 |
| url | https://arxiv.org/abs/2402.01963 |