# KenMeSH: Knowledge-enhanced End-to-end Biomedical Text Labelling

arXiv: https://arxiv.org/abs/2203.06835 · HTML: https://mlsanity.com/paper/2203.06835/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 14 Mar 2022.

## Builds on (most cited first, 10 of 10)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/1809.05679/) (2018)
- [Neural Machine Translation in Linear Time](https://mlsanity.com/paper/1610.10099/) (2016)
- [Large-scale Multi-label Text Classification - Revisiting Neural Networks](https://mlsanity.com/paper/1312.5419/) (2013)
- [Multi-Label Text Classification using Attention-based Graph Neural Network](https://mlsanity.com/paper/2003.11644/) (2020)
- [Semantic-Unit-Based Dilated Convolution for Multi-Label Text Classification](https://mlsanity.com/paper/1808.08561/) (2018)
- [Disease Normalization with Graph Embeddings](https://mlsanity.com/paper/2010.12925/) (2020)

## Built on by (most cited first, 3 of 3)

- [Auxiliary Knowledge-Induced Learning for Automatic Multi-Label Medical Document Classification](https://mlsanity.com/paper/2405.19084/) (2024)
- [MeSHup: A Corpus for Full Text Biomedical Document Indexing](https://mlsanity.com/paper/2204.13604/) (2022)
- [LabelCoRank: Revolutionizing Long Tail Multi-Label Classification with Co-Occurrence Reranking](https://mlsanity.com/paper/2503.07968/) (2025)

## Abstract (arXiv)

Currently, Medical Subject Headings (MeSH) are manually assigned to every biomedical article published and subsequently recorded in the PubMed database to facilitate retrieving relevant information. With the rapid growth of the PubMed database, large-scale biomedical document indexing becomes increasingly important. MeSH indexing is a challenging task for machine learning, as it needs to assign multiple labels to each article from an extremely large hierachically organized collection. To address this challenge, we propose KenMeSH, an end-to-end model that combines new text features and a dynamic Knowledge-enhanced mask attention that integrates document features with MeSH label hierarchy and journal correlation features to index MeSH terms. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures.