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

arXiv: https://arxiv.org/abs/2402.01963 · HTML: https://mlsanity.com/paper/2402.01963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 3 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Overview of BioASQ 2021: The ninth BioASQ challenge on Large-Scale Biomedical Semantic Indexing and Question Answering](https://mlsanity.com/paper/2106.14885/) (2021)
- [A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines](https://mlsanity.com/paper/1801.01586/) (2018)
- [Ranking-Based Autoencoder for Extreme Multi-label Classification](https://mlsanity.com/paper/1904.05937/) (2019)
- [AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction](https://mlsanity.com/paper/1802.08465/) (2018)
- [Target-Embedding Autoencoders for Supervised Representation Learning](https://mlsanity.com/paper/2001.08345/) (2020)

## Abstract (arXiv)

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.