# Using Neural Networks for Relation Extraction from Biomedical Literature

arXiv: https://arxiv.org/abs/1905.11391 · HTML: https://mlsanity.com/paper/1905.11391/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2019.

## Built on by (most cited first, 2 of 2)

- [Claim Detection in Biomedical Twitter Posts](https://mlsanity.com/paper/2104.11639/) (2021)
- [Recovering Patient Journeys: A Corpus of Biomedical Entities and Relations on Twitter (BEAR)](https://mlsanity.com/paper/2204.09952/) (2022)

## Abstract (arXiv)

Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.