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dc.contributor.authorTasnim, N.
dc.contributor.authorShihab, I.
dc.contributor.authorSushmit, A.S.
dc.contributor.authorBethard, S.
dc.contributor.authorSadeque, F.
dc.date.accessioned2022-10-24T23:51:23Z
dc.date.available2022-10-24T23:51:23Z
dc.date.issued2022
dc.identifier.citationNazia Tasnim, Md. Istiak Shihab, Asif Shahriyar Sushmit, Steven Bethard, and Farig Sadeque. 2022. TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in Bangla. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1524–1530, Seattle, United States. Association for Computational Linguistics.
dc.identifier.isbn9781955917803
dc.identifier.doi10.18653/v1/2022.semeval-1.209
dc.identifier.urihttp://hdl.handle.net/10150/666488
dc.description.abstractBiological and healthcare domains, artistic works, and organization names can all have nested, overlapping, discontinuous entity mentions that may be syntactically or semantically ambiguous in practice. Traditional sequence tagging algorithms are unable to recognize these complex mentions because they violate the assumptions upon which sequence tagging schemes are founded. In this paper, we describe our contribution to SemEval 2022 Task 11 on identifying such complex named entities. We leveraged an ensemble of ELECTRA-based models exclusively pretrained on the Bangla language with ELECTRA-based monolingual models pretrained on English to achieve competitive performance. Besides providing a system description, we also present the outcomes of our experiments on architectural decisions, dataset augmentations and post-competition findings. © 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.titleTEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in Bangla
dc.typeProceedings
dc.typetext
dc.contributor.departmentSchool of Information, University of Arizona
dc.identifier.journalSemEval 2022 - 16th International Workshop on Semantic Evaluation, 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.journaltitleSemEval 2022 - 16th International Workshop on Semantic Evaluation, Proceedings of the Workshop
refterms.dateFOA2022-10-24T23:51:23Z


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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.