Extracting Inter-Sentence Relations for Associating Biological Context with Events in Biomedical Texts
Hein, Paul D
Thumsi, Shraddha S
Hendryx, Sean M
Morrison, Clayton T
AffiliationUniv Arizona, Sch Informat
Univ Arizona, Dept Comp Sci
Univ Arizona, Dept Linguist
Univ Arizona, Dept Mol & Cellular Biol
Knowledge based systems
Biological information theory
inter-sentence relation extraction
MetadataShow full item record
PublisherIEEE COMPUTER SOC
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.
Rights© 2019 IEEE.
Collection InformationThis 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 firstname.lastname@example.org.
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.
VersionFinal accepted manuscript
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