# Abstractified Multi-instance Learning (AMIL) for Biomedical Relation Extraction

arXiv: https://arxiv.org/abs/2110.12501 · HTML: https://mlsanity.com/paper/2110.12501/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 24 Oct 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation](https://mlsanity.com/paper/1810.10147/) (2018)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Enriching Pre-trained Language Model with Entity Information for Relation Classification](https://mlsanity.com/paper/1905.08284/) (2019)
- [Relation Classification via Recurrent Neural Network](https://mlsanity.com/paper/1508.01006/) (2015)
- [Fine-tuning Pre-Trained Transformer Language Models to Distantly Supervised Relation Extraction](https://mlsanity.com/paper/1906.08646/) (2019)
- [Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix](https://mlsanity.com/paper/1705.03995/) (2017)
- [A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction](https://mlsanity.com/paper/2005.12565/) (2020)

## Built on by (most cited first, 7 of 7)

- [Fine-grained Contrastive Learning for Relation Extraction](https://mlsanity.com/paper/2205.12491/) (2022)
- [BioAug: Conditional Generation based Data Augmentation for Low-Resource Biomedical NER](https://mlsanity.com/paper/2305.10647/) (2023)
- [End-to-End Models for Chemical-Protein Interaction Extraction: Better Tokenization and Span-Based Pipeline Strategies](https://mlsanity.com/paper/2304.01344/) (2023)
- [An Overview of Distant Supervision for Relation Extraction with a Focus on Denoising and Pre-training Methods](https://mlsanity.com/paper/2207.08286/) (2022)
- [MedDistant19: Towards an Accurate Benchmark for Broad-Coverage Biomedical Relation Extraction](https://mlsanity.com/paper/2204.04779/) (2022)
- [Enhancing Biomedical Knowledge Discovery for Diseases: An Open-Source Framework Applied on Rett Syndrome and Alzheimer's Disease](https://mlsanity.com/paper/2407.13492/) (2024)
- [Reduction of Supervision for Biomedical Knowledge Discovery](https://mlsanity.com/paper/2504.09582/) (2025)

## Abstract (arXiv)

Relation extraction in the biomedical domain is a challenging task due to a lack of labeled data and a long-tail distribution of fact triples. Many works leverage distant supervision which automatically generates labeled data by pairing a knowledge graph with raw textual data. Distant supervision produces noisy labels and requires additional techniques, such as multi-instance learning (MIL), to denoise the training signal. However, MIL requires multiple instances of data and struggles with very long-tail datasets such as those found in the biomedical domain. In this work, we propose a novel reformulation of MIL for biomedical relation extraction that abstractifies biomedical entities into their corresponding semantic types. By grouping entities by types, we are better able to take advantage of the benefits of MIL and further denoise the training signal. We show this reformulation, which we refer to as abstractified multi-instance learning (AMIL), improves performance in biomedical relationship extraction. We also propose a novel relationship embedding architecture that further improves model performance.