{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T08:36:32Z","timestamp":1771576592138,"version":"3.50.1"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"18","license":[{"start":{"date-parts":[[2016,10,2]],"date-time":"2016-10-02T00:00:00Z","timestamp":1475366400000},"content-version":"vor","delay-in-days":2965,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/2.0\/uk\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2008,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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 \u2018average biologist\u2019 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.<\/jats:p>\n               <jats:p>Results: The annotation scheme was applied to a large corpus in a controlled effort by eight independent annotators, where three 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.<\/jats:p>\n               <jats:p>Contact: \u00a0shatkay@cs.queensu.ca<\/jats:p>","DOI":"10.1093\/bioinformatics\/btn381","type":"journal-article","created":{"date-parts":[[2008,8,22]],"date-time":"2008-08-22T00:33:56Z","timestamp":1219365236000},"page":"2086-2093","source":"Crossref","is-referenced-by-count":78,"title":["Multi-dimensional classification of biomedical text: Toward automated, practical provision of high-utility text to diverse users"],"prefix":"10.1093","volume":"24","author":[{"given":"Hagit","family":"Shatkay","sequence":"first","affiliation":[{"name":"1 The Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, Ontario, Canada, 2Department of Medicine, 3Department of Human Genetics, Computation Institute, and Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL and 4National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fengxia","family":"Pan","sequence":"additional","affiliation":[{"name":"1 The Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, Ontario, Canada, 2Department of Medicine, 3Department of Human Genetics, Computation Institute, and Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL and 4National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrey","family":"Rzhetsky","sequence":"additional","affiliation":[{"name":"1 The Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, Ontario, Canada, 2Department of Medicine, 3Department of Human Genetics, Computation Institute, and Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL and 4National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, USA"},{"name":"1 The Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, Ontario, Canada, 2Department of Medicine, 3Department of Human Genetics, Computation Institute, and Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL and 4National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"W. John","family":"Wilbur","sequence":"additional","affiliation":[{"name":"1 The Computational Biology and Machine Learning Lab, School of Computing, Queen's University, Kingston, Ontario, Canada, 2Department of Medicine, 3Department of Human Genetics, Computation Institute, and Institute for Genomics and Systems Biology, University of Chicago, Chicago, IL and 4National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2008,8,20]]},"reference":[{"key":"2023020211115982900_B1","article-title":"Automatic extraction of biological information from scientific text: protein-protein interactions","author":"Blaschke","year":"1999","journal-title":"Proceedings of International Conference on Intelligent Systems for Molecular Biology (ISMB)"},{"key":"2023020211115982900_B2","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","article-title":"Learning multi-label scene classification","volume":"37","author":"Boutell","year":"2004","journal-title":"Pattern Recognit."},{"key":"2023020211115982900_B3","author":"Chang","year":"2001","journal-title":"LIBSVM: a library for support vector machines."},{"key":"2023020211115982900_B4","article-title":"Constructing biological knowledge bases by extracting information from text sources","author":"Craven","year":"1999","journal-title":"Proceedings of International Conference on Intelligent Systems for Molecular Biology (ISMB)"},{"key":"2023020211115982900_B5","first-page":"147","article-title":"Natural language processing and systems biology. 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