Discovering Objective Functions for Tagging Medical Text Concepts
Abstract
This research demonstrates the use of genetic programming to derive the objective function that ranks the candidate concepts and selects the set of best matching concepts for a sentence within medical text. A short set of example primitive and linguistic variables was input into the GP process, and a set of manually tagged sentences extracted from the literature was used to derive different objective functions potentially suitable for tagging. This proof-of-concept demonstrates the potential of this approach to simplify automated semantic tagging and to identify some of the likely challenges of applying the GP approach to complex linguistics problems of this nature.
Recommended Citation
G. J. Shannon et al., "Discovering Objective Functions for Tagging Medical Text Concepts," Proceedings of the 2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (2014, Honolulu, HI), Institute of Electrical and Electronics Engineers (IEEE), May 2014.
The definitive version is available at https://doi.org/10.1109/CIBCB.2014.6845528
Meeting Name
2014 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2014 (2014: May 21-24, Honolulu, HI)
Department(s)
Engineering Management and Systems Engineering
Second Department
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-1479945368
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2014 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
24 May 2014
