Summary of Improving Large-scale K-nearest Neighbor Text Categorization with Label Autoencoders, by Francisco J. Ribadas-pena et al.
Improving Large-Scale k-Nearest Neighbor Text Categorization with Label Autoencoders
by Francisco J. Ribadas-Pena, Shuyuan Cao, Víctor M. Darriba Bilbao
First submitted to arxiv on: 3 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Information Retrieval (cs.IR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multi-label lazy learning approach is introduced for automatic semantic indexing in large document collections, tackling complex and structured label vocabularies with high inter-label correlation. This evolution of the k-Nearest Neighbors algorithm utilizes a large autoencoder to map the large label space to a reduced latent space and regenerate predicted labels. The method is evaluated on a large portion of the MEDLINE biomedical document collection using Medical Subject Headings (MeSH) as a controlled vocabulary, exploring various document representation approaches and label autoencoder configurations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand and categorize huge collections of documents by developing a new way to connect labels together. It uses a special type of machine learning called multi-label lazy learning. This approach is useful for organizing and searching large amounts of text, like biomedical papers. |
Keywords
* Artificial intelligence * Autoencoder * Latent space * Machine learning
