• Login
    View Item 
    •   Home
    • UA Faculty Research
    • UA Faculty Publications
    • View Item
    •   Home
    • UA Faculty Research
    • UA Faculty Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Extracting Inter-Sentence Relations for Associating Biological Context with Events in Biomedical Texts

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    context.pdf
    Size:
    7.076Mb
    Format:
    PDF
    Description:
    Final Accepted Manuscript
    Download
    Author
    Noriega-Atala, Enrique
    Hein, Paul D
    Thumsi, Shraddha S
    Wong, Zechy
    Wang, Xia
    Hendryx, Sean M
    Morrison, Clayton T
    Affiliation
    Univ Arizona, Sch Informat
    Univ Arizona, Dept Comp Sci
    Univ Arizona, Dept Linguist
    Univ Arizona, Dept Mol & Cellular Biol
    Issue Date
    2020-12-08
    Keywords
    Containers
    Knowledge based systems
    Data mining
    Linguistics
    Feature extraction
    Biological information theory
    Context
    inter-sentence relation extraction
    NLP
    data mining
    bioinformatics
    Show allShow less
    
    Metadata
    Show full item record
    Publisher
    IEEE COMPUTER SOC
    Citation
    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.
    Journal
    IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
    Rights
    © 2019 IEEE
    Collection 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
    1545-5963
    EISSN
    1557-9964
    PubMed ID
    30869629
    DOI
    10.1109/TCBB.2019.2904231
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1109/TCBB.2019.2904231
    Scopus Count
    Collections
    UA Faculty Publications

    entitlement

    Related articles

    • Exploiting sequence labeling framework to extract document-level relations from biomedical texts.
    • Authors: Li Z, Yang Z, Xiang Y, Luo L, Sun Y, Lin H
    • Issue date: 2020 Mar 27
    • Detecting causality from online psychiatric texts using inter-sentential language patterns.
    • Authors: Wu JL, Yu LC, Chang PC
    • Issue date: 2012 Jul 18
    • A generalizable NLP framework for fast development of pattern-based biomedical relation extraction systems.
    • Authors: Peng Y, Torii M, Wu CH, Vijay-Shanker K
    • Issue date: 2014 Aug 23
    • Exploiting graph kernels for high performance biomedical relation extraction.
    • Authors: Panyam NC, Verspoor K, Cohn T, Ramamohanarao K
    • Issue date: 2018 Jan 30
    • Event extraction with complex event classification using rich features.
    • Authors: Miwa M, Saetre R, Kim JD, Tsujii J
    • Issue date: 2010 Feb
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.