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dc.contributor.author Terčon, Luka
dc.contributor.author Ljubešić, Nikola
dc.date.accessioned 2023-05-16T06:56:43Z
dc.date.available 2023-05-16T06:56:43Z
dc.date.issued 2023-05-10
dc.identifier.uri http://hdl.handle.net/11356/1830
dc.description The model for lemmatisation of standard Serbian was built with the CLASSLA-Stanza tool (https://github.com/clarinsi/classla) by training on the SETimes.SR training corpus (http://hdl.handle.net/11356/1200) combined with the Serbian non-standard training corpus ReLDI-NormTagNER-sr (http://hdl.handle.net/11356/1794) and using the srLex inflectional lexicon (http://hdl.handle.net/11356/1233). The estimated F1 of the lemma annotations is ~98.02. The difference to the previous version is that this version was trained on a combination of the standard (SETimes.SR) and non-standard (ReLDI-NormTagNER-sr) Serbian training corpora.
dc.language.iso srp
dc.publisher Jožef Stefan Institute
dc.relation.isreferencedby http://dx.doi.org/10.18653/v1/W19-3704
dc.relation.replaces http://hdl.handle.net/11356/1355
dc.rights Creative Commons - Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
dc.rights.uri https://creativecommons.org/licenses/by-sa/4.0/
dc.rights.label PUB
dc.source.uri https://github.com/clarinsi/classla
dc.subject lemmatisation
dc.subject language model
dc.title The CLASSLA-Stanza model for lemmatisation of standard Serbian 2.1
dc.type toolService
metashare.ResourceInfo#ContentInfo.detailedType tool
metashare.ResourceInfo#ResourceComponentType#ToolServiceInfo.languageDependent true
has.files yes
branding CLARIN.SI data & tools
contact.person Nikola Ljubešić nikola.ljubesic@ijs.si Jožef Stefan Institute
contact.person Luka Terčon luka.tercon@gmail.com Faculty of Computer and Information Science, University of Ljubljana
sponsor ARRS (Slovenian Research Agency) P6-0411 Language Resources and Technologies for Slovene nationalFunds
sponsor Jožef Stefan Institute CLARIN CLARIN.SI nationalFunds
sponsor ARRS (Slovenian Research Agency) J7-4642 MEZZANINE nationalFunds
sponsor Connecting Europe Facility (CEF) Telecom INEA/CEF/ICT/A2020/2278341 MaCoCu - Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages Other
files.count 1
files.size 110024491


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