# Simultaneously Linking Entities and Extracting Relations from Biomedical Text Without Mention-level Supervision

arXiv: https://arxiv.org/abs/1912.01070 · HTML: https://mlsanity.com/paper/1912.01070/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes](https://mlsanity.com/paper/2204.06584/) (2022)

## Abstract (arXiv)

Understanding the meaning of text often involves reasoning about entities and their relationships. This requires identifying textual mentions of entities, linking them to a canonical concept, and discerning their relationships. These tasks are nearly always viewed as separate components within a pipeline, each requiring a distinct model and training data. While relation extraction can often be trained with readily available weak or distant supervision, entity linkers typically require expensive mention-level supervision -- which is not available in many domains. Instead, we propose a model which is trained to simultaneously produce entity linking and relation decisions while requiring no mention-level annotations. This approach avoids cascading errors that arise from pipelined methods and more accurately predicts entity relationships from text. We show that our model outperforms a state-of-the art entity linking and relation extraction pipeline on two biomedical datasets and can drastically improve the overall recall of the system.