Extracting Inter-Sentence Relations for Associating Biological Context with Events in Biomedical Texts
Author
Noriega-Atala, EnriqueHein, Paul D
Thumsi, Shraddha S
Wong, Zechy
Wang, Xia
Hendryx, Sean M
Morrison, Clayton T
Affiliation
Univ Arizona, Sch InformatUniv Arizona, Dept Comp Sci
Univ Arizona, Dept Linguist
Univ Arizona, Dept Mol & Cellular Biol
Issue Date
2020-12-08Keywords
ContainersKnowledge based systems
Data mining
Linguistics
Feature extraction
Biological information theory
Context
inter-sentence relation extraction
NLP
data mining
bioinformatics
Metadata
Show full item recordPublisher
IEEE COMPUTER SOCCitation
E. 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 IEEECollection Information
This 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.Abstract
We 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.ISSN
1545-5963EISSN
1557-9964PubMed ID
30869629Version
Final accepted manuscriptae974a485f413a2113503eed53cd6c53
10.1109/TCBB.2019.2904231
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