Annotating Norwegian language varieties on Twitter for Part-of-speech
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AV994YTQV" target="_blank" >RIV/00216208:11320/22:V994YTQV - isvavai.cz</a>
Result on the web
<a href="https://aclanthology.org/2022.vardial-1.7" target="_blank" >https://aclanthology.org/2022.vardial-1.7</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Annotating Norwegian language varieties on Twitter for Part-of-speech
Original language description
Norwegian Twitter data poses an interesting challenge for Natural Language Processing (NLP) tasks. These texts are difficult for models trained on standardized text in one of the two Norwegian written forms (Bokmål and Nynorsk), as they contain both the typical variation of social media text, as well as a large amount of dialectal variety. In this paper we present a novel Norwegian Twitter dataset annotated with POS-tags. We show that models trained on Universal Dependency (UD) data perform worse when evaluated against this dataset, and that models trained on Bokmål generally perform better than those trained on Nynorsk. We also see that performance on dialectal tweets is comparable to the written standards for some models. Finally we perform a detailed analysis of the errors that models commonly make on this data.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the Ninth Workshop on NLP for Similar Languages, Varieties and Dialects
ISBN
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ISSN
2951-2093
e-ISSN
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Number of pages
6
Pages from-to
64-69
Publisher name
Association for Computational Linguistics
Place of publication
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Event location
Gyeongju, Republic of Korea
Event date
Jan 1, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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