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dc.contributor.authorWiechetek, Linda
dc.contributor.authorPirinen, Flammie
dc.contributor.authorGaup, Børre
dc.contributor.authorArgese, Chiara
dc.contributor.authorOmma, Thomas
dc.date.accessioned2023-01-26T10:13:51Z
dc.date.available2023-01-26T10:13:51Z
dc.date.issued2022-08-30
dc.description.abstractMachine learning is the dominating paradigm in natural language processing nowadays. It requires vast amounts of manually annotated or synthetically generated text data. In the GiellaLT infrastructure, on the other hand, we have worked with rule-based methods, where the linguistis have full control over the development the tools. In this article we uncover the myth of machine learning being cheaper than a rule-based approach by showing how much work there is behind data generation, either via corpus annotation or creating tools that automatically mark-up the corpus. Earlier we have shown that the correction of grammatical errors, in particular compound errors, benefit from hybrid methods. Agreement errors, on the other other hand, are to a higher degree dependent on the larger grammatical context. Our experiments show that machine learning methods for this error type, even when supplemented by rule-based methods generating massive data, can not compete with the state-of-the-art rule-based approach.en_US
dc.identifier.citationWiechetek, Pirinen, Gaup, Argese, Omma. Mii *eai leat gal vuollánan – Vi *ha neimen ikke gitt opp. Nordlyd. 2022en_US
dc.identifier.cristinIDFRIDAID 2114160
dc.identifier.doi10.7557/12.6346
dc.identifier.issn0332-7531
dc.identifier.issn1503-8599
dc.identifier.urihttps://hdl.handle.net/10037/28380
dc.language.isonoben_US
dc.publisherSeptentrio Academic Publishingen_US
dc.relation.journalNordlyd
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0en_US
dc.rightsAttribution-NonCommercial 4.0 International (CC BY-NC 4.0)en_US
dc.titleMii *eai leat gal vuollánan – Vi *ha neimen ikke gitt oppen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)