Bridging semantics and syntax with graph algorithms—state-of-the-art of extracting biomedical relations
Name
Bridging_Semantics_and_Syntax_with_Graph_Algorithms16.pdf
Description
Accepted version
Size
1.08 MB
Format
Adobe PDF
Checksum (MD5)
e2cd9c86179283bea07339c636dd2e9b
Author(s)
Luo, Yuan
Uzuner, Özlem
Szolovits, Peter
Date Issued
February 2016
Journal
Briefings in Bioinformatics
Publisher
Oxford University Press
Citation
Luo, Yuan et al. "Bridging semantics and syntax with graph algorithms—state-of-the-art of extracting biomedical relations." Briefings in Bioinformatics 18, 1 (2017): 160–178 © 2016 The Author(s)
Version
Author's final manuscript
Abstract
Research on extracting biomedical relations has received growing attention recently, with numerous biological and clinical applications including those in pharmacogenomics, clinical trial screening and adverse drug reaction detection. The ability to accurately capture both semantic and syntactic structures in text expressing these relations becomes increasingly critical to enable deep understanding of scientific papers and clinical narratives. Shared task challenges have been organized by both bioinformatics and clinical informatics communities to assess and advance the state-of-the-art research. Significant progress has been made in algorithm development and resource construction. In particular, graph-based approaches bridge semantics and syntax, often achieving the best performance in shared tasks. However, a number of problems at the frontiers of biomedical relation extraction continue to pose interesting challenges and present opportunities for great improvement and fruitful research. In this article, we place biomedical relation extraction against the backdrop of its versatile applications, present a gentle introduction to its general pipeline and shared resources, review the current state-of-the-art in methodology advancement, discuss limitations and point out several promising future directions. Keywords: biomedical relation extraction; natural language processing; graph mining; machine learning; scientific literature; clinical narratives
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1093/bib/bbw001
