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dc.contributor.authorBölücü, Necva
dc.contributor.authorCan, Burcu
dc.contributor.editorCalzolari, Nicoletta
dc.contributor.editorBéchet, Frédéric
dc.contributor.editorBlache, Philippe
dc.contributor.editorChoukri, Khalid
dc.contributor.editorCieri, Christopher
dc.contributor.editorDeclerck, Thierry
dc.contributor.editorGoggi, Sara
dc.contributor.editorIsahara, Hitoshi
dc.contributor.editorMaegaard, Bente
dc.contributor.editorMariani, Joseph
dc.contributor.editorMazo, Hélène
dc.contributor.editorOdijk, Jan
dc.contributor.editorPiperidis, Stelios
dc.date.accessioned2022-05-12T14:36:30Z
dc.date.available2022-05-12T14:36:30Z
dc.date.issued2022-06-01
dc.identifier.citationBölücü, N. and Can, B. (2022) Turkish universal conceptual cognitive annotation, In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 89–99, Marseille, France. European Language Resources Association.en
dc.identifier.isbn9791095546726en
dc.identifier.issn2522-2686en
dc.identifier.urihttp://hdl.handle.net/2436/624754
dc.description© 2022 The Authors. Published by Language Resources and Evaluation Conference. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: http://www.lrec-conf.org/proceedings/lrec2022/index.htmlen
dc.description.abstractUniversal Conceptual Cognitive Annotation (UCCA) is a cross-lingual semantic annotation framework that provides an easy annotation without any requirement for linguistic background. UCCA-annotated datasets have been already released in English, French, and German. In this paper, we introduce the first UCCA-annotated Turkish dataset that currently involves 50 sentences obtained from the METU-Sabanci Turkish Treebank. We followed a semi-automatic annotation approach, where an external semantic parser is utilised for an initial annotation of the dataset, which is partially accurate and requires refinement. We manually revised the annotations obtained from the semantic parser that are not in line with the UCCA rules that we defined for Turkish. We used the same external semantic parser for evaluation purposes and conducted experiments with both zero-shot and few-shot learning. This is the initial version of the annotated dataset and we are currently extending the dataset. We are releasing the current Turkish UCCA annotation guideline along with the annotated dataset.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherEuropean Language Resources Associationen
dc.relation.urlhttps://aclanthology.org/2022.lrec-1.10en
dc.subjectsematic parsingen
dc.subjectcomputational linguisticsen
dc.titleTurkish universal conceptual cognitive annotationen
dc.typeConference contributionen
dc.date.updated2022-05-12T14:05:36Z
dc.conference.nameLREC 2022 - 13th International Conference on Language Resources and Evaluation
dc.conference.locationMarseille, France
pubs.finish-date2022-06-23
pubs.start-date2022-06-21
dc.date.accepted2022-04-04
rioxxterms.funderUniversity of Wolverhamptonen
rioxxterms.identifier.projectUOW12052022BCen
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by-nc/4.0/en
rioxxterms.licenseref.startdate2022-06-15en
dc.source.beginpage89
dc.source.endpage99
refterms.dateFCD2022-05-12T14:36:07Z
refterms.versionFCDVoR
refterms.dateFOA2022-06-16T03:11:25Z


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