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Adv Bioinformatics. 2012; 2012: 391574.
Published online May 22, 2012. doi:  10.1155/2012/391574
PMCID: PMC3364545
Applications of Natural Language Processing in Biodiversity Science
Anne E. Thessen, 1 * Hong Cui, 2 and Dmitry Mozzherin 1
1Center for Library and Informatics, Marine Biological Laboratory, 7 MBL Street, Woods Hole, MA 02543, USA
2School of Information Resources and Library Science, University of Arizona, Tucson, AZ 85719, USA
*Anne E. Thessen: athessen/at/mbl.edu
Academic Editor: Jörg Hakenberg
Received November 4, 2011; Accepted February 15, 2012.
Abstract
Centuries of biological knowledge are contained in the massive body of scientific literature, written for human-readability but too big for any one person to consume. Large-scale mining of information from the literature is necessary if biology is to transform into a data-driven science. A computer can handle the volume but cannot make sense of the language. This paper reviews and discusses the use of natural language processing (NLP) and machine-learning algorithms to extract information from systematic literature. NLP algorithms have been used for decades, but require special development for application in the biological realm due to the special nature of the language. Many tools exist for biological information extraction (cellular processes, taxonomic names, and morphological characters), but none have been applied life wide and most still require testing and development. Progress has been made in developing algorithms for automated annotation of taxonomic text, identification of taxonomic names in text, and extraction of morphological character information from taxonomic descriptions. This manuscript will briefly discuss the key steps in applying information extraction tools to enhance biodiversity science.
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