# Multi-label Few/Zero-shot Learning with Knowledge Aggregated from Multiple Label Graphs

arXiv: https://arxiv.org/abs/2010.07459 · HTML: https://mlsanity.com/paper/2010.07459/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 19 of 19)

- [Revisiting Transformer-based Models for Long Document Classification](https://mlsanity.com/paper/2204.06683/) (2022)
- [Knowledge Injected Prompt Based Fine-tuning for Multi-label Few-shot ICD Coding](https://mlsanity.com/paper/2210.03304/) (2022)
- [A Unified Review of Deep Learning for Automated Medical Coding](https://mlsanity.com/paper/2201.02797/) (2022)
- [Multi-label Few-shot ICD Coding as Autoregressive Generation with Prompt](https://mlsanity.com/paper/2211.13813/) (2022)
- [Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction](https://mlsanity.com/paper/2209.05987/) (2022)
- [Automated clinical coding using off-the-shelf large language models](https://mlsanity.com/paper/2310.06552/) (2023)
- [Exploring LLM Multi-Agents for ICD Coding](https://mlsanity.com/paper/2406.15363/) (2024)
- [Few-shot learning for medical text: A systematic review](https://mlsanity.com/paper/2204.14081/) (2022)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning](https://mlsanity.com/paper/2104.01666/) (2021)
- [Few Shot Learning for Medical Imaging: A Comparative Analysis of Methodologies and Formal Mathematical Framework](https://mlsanity.com/paper/2305.04401/) (2023)
- [Stratified Sampling for Extreme Multi-Label Data](https://mlsanity.com/paper/2103.03494/) (2021)
- [Open Vocabulary Multi-Label Classification with Dual-Modal Decoder on Aligned Visual-Textual Features](https://mlsanity.com/paper/2208.09562/) (2022)
- [TagRec++: Hierarchical Label Aware Attention Network for Question Categorization](https://mlsanity.com/paper/2208.05152/) (2022)
- [Automated Clinical Coding for Outpatient Departments](https://mlsanity.com/paper/2312.13533/) (2023)
- [Cross Encoding as Augmentation: Towards Effective Educational Text Classification](https://mlsanity.com/paper/2305.18977/) (2023)
- [Multi-Label Bayesian Active Learning with Inter-Label Relationships](https://mlsanity.com/paper/2411.17941/) (2024)
- [Structured Information Matters: Explainable ICD Coding with Patient-Level Knowledge Graphs](https://mlsanity.com/paper/2509.09699/) (2025)
- [Beyond ICD Codes: Semantic IDs for Generative Medical Coding](https://mlsanity.com/paper/2610.19301/) (2026)

## Abstract (arXiv)

Few/Zero-shot learning is a big challenge of many classifications tasks, where a classifier is required to recognise instances of classes that have very few or even no training samples. It becomes more difficult in multi-label classification, where each instance is labelled with more than one class. In this paper, we present a simple multi-graph aggregation model that fuses knowledge from multiple label graphs encoding different semantic label relationships in order to study how the aggregated knowledge can benefit multi-label zero/few-shot document classification. The model utilises three kinds of semantic information, i.e., the pre-trained word embeddings, label description, and pre-defined label relations. Experimental results derived on two large clinical datasets (i.e., MIMIC-II and MIMIC-III) and the EU legislation dataset show that methods equipped with the multi-graph knowledge aggregation achieve significant performance improvement across almost all the measures on few/zero-shot labels.