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dc.contributor.authorHungerford, J.
dc.contributor.authorChan, Y.S.
dc.contributor.authorMacBride, J.
dc.contributor.authorGyori, B.M.
dc.contributor.authorZupon, A.
dc.contributor.authorTang, Z.
dc.contributor.authorLaparra, E.
dc.contributor.authorQiu, H.
dc.contributor.authorMin, B.
dc.contributor.authorZverev, Y.
dc.contributor.authorHilverman, C.
dc.contributor.authorThomas, M.
dc.contributor.authorAndrews, W.
dc.contributor.authorAlcock, K.
dc.contributor.authorZhang, Z.
dc.contributor.authorReynolds, M.
dc.contributor.authorSurdeanu, M.
dc.contributor.authorBethard, S.
dc.contributor.authorSharp, R.
dc.date.accessioned2022-10-24T23:51:21Z
dc.date.available2022-10-24T23:51:21Z
dc.date.issued2022
dc.identifier.citationMihai Surdeanu, John Hungerford, Yee Seng Chan, Jessica MacBride, Benjamin Gyori, Andrew Zupon, Zheng Tang, Haoling Qiu, Bonan Min, Yan Zverev, Caitlin Hilverman, Max Thomas, Walter Andrews, Keith Alcock, Zeyu Zhang, Michael Reynolds, Steven Bethard, Rebecca Sharp, and Egoitz Laparra. 2022. Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction. In Proceedings of the Second Workshop on Bridging Human--Computer Interaction and Natural Language Processing, pages 1–10, Seattle, Washington. Association for Computational Linguistics.
dc.identifier.isbn9781955917902
dc.identifier.doi10.18653/v1/2022.hcinlp-1.1
dc.identifier.urihttp://hdl.handle.net/10150/666484
dc.description.abstractAn existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none. When one does exist, as the information needs shift, it must be continually extended. This is a slow and tedious task, and one that does not scale well. Here we propose an interactive tool that allows a taxonomy to be built or extended rapidly and with a human in the loop to control precision. We apply insights from text summarization and information extraction to reduce the search space dramatically, then leverage modern pretrained language models to perform contextualized clustering of the remaining concepts to yield candidate nodes for the user to review. We show this allows a user to consider as many as 200 taxonomy concept candidates an hour to quickly build or extend a taxonomy to better fit information needs. © 2022 Association for Computational Linguistics.
dc.language.isoen
dc.publisherAssociation for Computational Linguistics (ACL)
dc.rightsCopyright © 2022 Association for Computational Linguistics. This is an open access article licensed on a Creative Commons Attribution 4.0 International License.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleTaxonomy Builder: A Data-driven and User-centric Tool for Streamlining Taxonomy Construction
dc.typeProceedings
dc.typetext
dc.contributor.departmentUniversity of Arizona
dc.identifier.journalHCI+NLP 2022 - 2nd Workshop on Bridging Human-Computer Interaction and Natural Language Processing, Proceedings of the Workshop
dc.description.noteOpen access journal
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.
dc.eprint.versionFinal published version
dc.source.journaltitleHCI+NLP 2022 - 2nd Workshop on Bridging Human-Computer Interaction and Natural Language Processing, Proceedings of the Workshop
refterms.dateFOA2022-10-24T23:51:22Z


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Copyright © 2022 Association for Computational Linguistics. This is an open access article licensed on a Creative Commons Attribution 4.0 International License.
Except where otherwise noted, this item's license is described as Copyright © 2022 Association for Computational Linguistics. This is an open access article licensed on a Creative Commons Attribution 4.0 International License.