# Jointly Learning Clinical Entities and Relations with Contextual Language Models and Explicit Context

arXiv: https://arxiv.org/abs/2102.11031 · HTML: https://mlsanity.com/paper/2102.11031/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 17 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Matching the Blanks: Distributional Similarity for Relation Learning](https://mlsanity.com/paper/1906.03158/) (2019)
- [Enriching Pre-trained Language Model with Entity Information for Relation Classification](https://mlsanity.com/paper/1905.08284/) (2019)
- [Joint entity recognition and relation extraction as a multi-head selection problem](https://mlsanity.com/paper/1804.07847/) (2018)
- [Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text](https://mlsanity.com/paper/1908.07721/) (2019)
- [Clinical Concept Extraction with Contextual Word Embedding](https://mlsanity.com/paper/1810.10566/) (2018)

## Abstract (arXiv)

We hypothesize that explicit integration of contextual information into an Multi-task Learning framework would emphasize the significance of context for boosting performance in jointly learning Named Entity Recognition (NER) and Relation Extraction (RE). Our work proves this hypothesis by segmenting entities from their surrounding context and by building contextual representations using each independent segment. This relation representation allows for a joint NER/RE system that achieves near state-of-the-art (SOTA) performance on both NER and RE tasks while beating the SOTA RE system at end-to-end NER & RE with a 49.07 F1.