Bioinformatics Advance Access published online on August 20, 2008
Bioinformatics, doi:10.1093/bioinformatics/btn381
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MULTI-DIMENSIONAL CLASSIFICATION of BIOMEDICAL TEXT: Toward Automated, Practical Provision of High-Utility Text to Diverse Users
1 The Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, Ontario, Canada
2 Departments of Medicine and Human Genetics, Computation Institute, and Institute for Genomics and Systems Biology, University of Chicago, Chicago, Illinois
3 National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, Maryland
*To whom correspondence should be addressed. Prof. Hagit Shatkay, E-mail: shatkay{at}cs.queensu.ca
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Abstract |
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Motivation: Much current research in biomedical text mining is concerned with serving biologists by extracting certain information from scientific text. We note that there is no "average biologist" client; different users have distinct needs. For instance, as noted in past evaluation efforts (Biocreative,TREC,KDD) database curators are often interested in sentences showing experimental evidence and methods. Conversely, lab scientists searching for known information about a protein may seek facts, typically stated with high confidence.
Text-mining systems can target specific end-users and become more effective, if the system can first identify text regions rich in the type of scientific content that is of interest to the user, retrieve documents that have many such regions, and focus on fact extraction from these regions. Here we study the ability to characterize and classify such text automatically.
We have recently introduced a multi-dimensional categorization and annotation scheme, developed to be applicable to a wide variety of biomedical documents and scientific statements, while intended to support specific biomedical retrieval and extraction tasks.
Results: The annotation scheme was applied to a large corpus in a controlled effort by 8 independent annotators, where 3 individual annotators independently tagged each sentence. We then trained and tested machine-learning classifiers to automatically categorize sentence fragments based on the annotation. We discuss here the issues involved in this task, and present an overview of the results. The latter strongly suggest that automatic annotation along most of the dimensions is highly feasible, and that this new framework for scientific sentence categorization is applicable in practice
Associate Editor: Prof. Thomas Lengauer
Received on May 25, 2008; revised on July 17, 2008; accepted on July 19, 2008
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