# Learning for Biomedical Information Extraction: Methodological Review of Recent Advances

arXiv: https://arxiv.org/abs/1606.07993 · HTML: https://mlsanity.com/paper/1606.07993/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2016.

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

- [A sequence-to-sequence approach for document-level relation extraction](https://mlsanity.com/paper/2204.01098/) (2022)
- [Data-Driven Information Extraction and Enrichment of Molecular Profiling Data for Cancer Cell Lines](https://mlsanity.com/paper/2307.00933/) (2023)

## Abstract (arXiv)

Biomedical information extraction (BioIE) is important to many applications, including clinical decision support, integrative biology, and pharmacovigilance, and therefore it has been an active research. Unlike existing reviews covering a holistic view on BioIE, this review focuses on mainly recent advances in learning based approaches, by systematically summarizing them into different aspects of methodological development. In addition, we dive into open information extraction and deep learning, two emerging and influential techniques and envision next generation of BioIE.