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dc.contributor.authorNoriega-Atala, Enrique
dc.contributor.authorHein, Paul D
dc.contributor.authorThumsi, Shraddha S
dc.contributor.authorWong, Zechy
dc.contributor.authorWang, Xia
dc.contributor.authorHendryx, Sean M
dc.contributor.authorMorrison, Clayton T
dc.date.accessioned2021-01-13T20:54:53Z
dc.date.available2021-01-13T20:54:53Z
dc.date.issued2020-12-08
dc.identifier.citationE. Noriega-Atala et al., "Extracting Inter-Sentence Relations for Associating Biological Context with Events in Biomedical Texts," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 17, no. 6, pp. 1895-1906, 1 Nov.-Dec. 2020, doi: 10.1109/TCBB.2019.2904231.en_US
dc.identifier.issn1545-5963
dc.identifier.pmid30869629
dc.identifier.doi10.1109/TCBB.2019.2904231
dc.identifier.urihttp://hdl.handle.net/10150/650754
dc.description.abstractWe present an analysis of the problem of identifying biological context and associating it with biochemical events described in biomedical texts. This constitutes a non-trivial, inter-sentential relation extraction task. We focus on biological context as descriptions of the species, tissue type, and cell type that are associated with biochemical events. We present a new corpus of open access biomedical texts that have been annotated by biology subject matter experts to highlight context-event relations. Using this corpus, we evaluate several classifiers for context-event association along with a detailed analysis of the impact of a variety of linguistic features on classifier performance. We find that gradient tree boosting performs by far the best, achieving an F1 of 0.865 in a cross-validation study.en_US
dc.language.isoenen_US
dc.publisherIEEE COMPUTER SOCen_US
dc.rights© 2019 IEEEen_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectContainersen_US
dc.subjectKnowledge based systemsen_US
dc.subjectData miningen_US
dc.subjectLinguisticsen_US
dc.subjectFeature extractionen_US
dc.subjectBiological information theoryen_US
dc.subjectContexten_US
dc.subjectinter-sentence relation extractionen_US
dc.subjectNLPen_US
dc.subjectdata miningen_US
dc.subjectbioinformaticsen_US
dc.titleExtracting Inter-Sentence Relations for Associating Biological Context with Events in Biomedical Textsen_US
dc.typeArticleen_US
dc.identifier.eissn1557-9964
dc.contributor.departmentUniv Arizona, Sch Informaten_US
dc.contributor.departmentUniv Arizona, Dept Comp Scien_US
dc.contributor.departmentUniv Arizona, Dept Linguisten_US
dc.contributor.departmentUniv Arizona, Dept Mol & Cellular Biolen_US
dc.identifier.journalIEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICSen_US
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleIEEE/ACM transactions on computational biology and bioinformatics
dc.source.volume17
dc.source.issue6
dc.source.beginpage1895
dc.source.endpage1906
refterms.dateFOA2021-01-13T20:55:10Z
dc.source.countryUnited States


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