The CLASSLA-Stanza model for UD dependency parsing of standard Serbian 2.1

Name: The CLASSLA-Stanza model for UD dependency parsing of standard Serbian 2.1
License: https://creativecommons.org/licenses/by-sa/4.0/

Terčon, Luka; Ljubešić, Nikola

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dc.contributor.author	Terčon, Luka
dc.contributor.author	Ljubešić, Nikola
dc.date.accessioned	2023-05-16T06:58:45Z
dc.date.available	2023-05-16T06:58:45Z
dc.date.issued	2023-05-10
dc.identifier.uri	http://hdl.handle.net/11356/1835
dc.description	The model for UD dependency parsing 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) and using the CLARIN.SI-embed.sr word embeddings (http://hdl.handle.net/11356/1789). The estimated LAS of the parser is ~89.83. The difference to the previous version of the model is that this version uses the new version of Serbian word embeddings.
dc.language.iso	srp
dc.publisher	Jožef Stefan Institute
dc.relation.isreferencedby	https://aclanthology.org/W19-3704/
dc.relation.replaces	http://hdl.handle.net/11356/1260
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	parsing
dc.subject	language model
dc.title	The CLASSLA-Stanza model for UD dependency parsing 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	2
files.size	199021780