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dc.contributor.authorThessen, Anne E.  Concept link
dc.contributor.authorCui, Hong  Concept link
dc.contributor.authorMozzherin, Dmitry  Concept link
dc.date.accessioned2012-06-20T18:06:18Z
dc.date.available2012-06-20T18:06:18Z
dc.date.issued2012
dc.identifier.citationAdvances in Bioinformatics 2012 (2012): 391574en_US
dc.identifier.urihttps://hdl.handle.net/1912/5235
dc.description© The Author(s), 2012. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Advances in Bioinformatics 2012 (2012): 391574, doi:10.1155/2012/391574.en_US
dc.description.abstractCenturies 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.en_US
dc.description.sponsorshipThis work was funded in part by the MacArthur Foundation Grant to the Encyclopedia of Life, the National Science Foundation Data Net Program Grant no. 0830976, and the National Science Foundation Emerging Front Grant no. 0849982.en_US
dc.format.mimetypeapplication/pdf
dc.language.isoen_USen_US
dc.publisherHindawi Publishingen_US
dc.relation.urihttps://doi.org/10.1155/2012/391574
dc.rightsAttribution 3.0 Unported*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/*
dc.titleApplications of natural language processing in biodiversity scienceen_US
dc.typeArticleen_US
dc.identifier.doi10.1155/2012/391574


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Attribution 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution 3.0 Unported