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dc.contributor.authorPirinen, Flammie
dc.contributor.authorMoshagen, Sjur Nørstebø
dc.contributor.authorKaalep, Heiki-Jaan
dc.date.accessioned2023-01-26T10:15:22Z
dc.date.available2023-01-26T10:15:22Z
dc.date.issued2022-08-30
dc.description.abstractIn this article, we study correction of spelling errors, specifically on how the spelling errors are made and how can we model them computationally in order to fix them. The article describes two different approaches to generating spelling correction suggestions for three Uralic languages: Estonian, North Sámi and South Sámi. The first approach of modelling spelling errors is rule-based, where experts write rules that describe the kind of errors are made, and these are compiled into finite-state automaton that models the errors. The second is data-based, where we show a machine learning algorithm a corpus of errors that humans have made, and it creates a neural network that can model the errors. Both approaches require collection of error corpora and understanding its contents; therefore we also describe the actual errors we have seen in detail. We find that while both approaches create error correction systems, with current resources the expert-build systems are still more reliable.en_US
dc.identifier.citationPirinen, Moshagen, Kaalep. You can’t suggest that?! Comparisons and improvements of speller error models . Nordlyd. 2022
dc.identifier.cristinIDFRIDAID 2114174
dc.identifier.doi10.7557/12.6349
dc.identifier.issn0332-7531
dc.identifier.issn1503-8599
dc.identifier.urihttps://hdl.handle.net/10037/28381
dc.language.isoengen_US
dc.publisherSeptentrio Academic Publishingen_US
dc.relation.journalNordlyd
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.titleYou can’t suggest that?! Comparisons and improvements of speller error modelsen_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)