New directions in biomedical text annotation: definitions, guidelines and corpus construction |
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| Authors: | W John Wilbur Andrey Rzhetsky and Hagit Shatkay |
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| Institution: | (1) National Center for Biotechnology Information NLM, NIH, Bethesda, MD, USA;(2) Department of Biomedical Informaticsand Department of Biology, Center for Computational Biology and Bioinformatics, Judith P. Sulzberger MD Columbia Genome Center, Columbia University, New York, NY, USA;(3) School of Computing, Queen's University, Kingston, ON, Canada |
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| Abstract: | Background While biomedical text mining is emerging as an important research area, practical results have proven difficult to achieve.
We believe that an important first step towards more accurate text-mining lies in the ability to identify and characterize
text that satisfies various types of information needs. We report here the results of our inquiry into properties of scientific
text that have sufficient generality to transcend the confines of a narrow subject area, while supporting practical mining
of text for factual information. Our ultimate goal is to annotate a significant corpus of biomedical text and train machine
learning methods to automatically categorize such text along certain dimensions that we have defined. |
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