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dc.contributor.authorBuch, Armin
dc.contributor.authorErschler, David
dc.contributor.authorJäger, Gerhard
dc.contributor.authorLupas, Andrei
dc.contributor.editorSaxena, Anju
dc.contributor.editorBorin, Lars
dc.date.accessioned2025-03-07T21:54:48Z
dc.date.available2025-03-07T21:54:48Z
dc.date.issued2013
dc.date.submitted2019-11-19 23:55
dc.date.submitted2020-01-07 16:47:06
dc.date.submitted2020-04-01T09:26:38Z
dc.identifier1006432
dc.identifierhttp://library.oapen.org/handle/20.500.12657/23712
dc.identifier.urihttps://doab-dev.siscern.org/handle/20.500.12854/168281
dc.description.abstractIn this paper, we discuss advantages of clustering approaches to automated language classification, describe distance measures used for this purpose, and present results of several proof-of-concept experiments. We advocate the use of probability based distances – those that take into account the distribution of relevant features across the language sample in question
dc.languageEnglish
dc.rightsopen access
dc.subject.otherlinguistic differences
dc.subject.otherthema EDItEUR::C Language and Linguistics::CF Linguistics
dc.titleChapter Towards automated language classification
dc.title.alternativeA clustering approach
dc.typechapter
oapen.identifier.doi10.1515/9783110305258.303
oapen.relation.isPublishedByaf2fbfcc-ee87-43d8-a035-afb9d7eef6a5
oapen.relation.isPartOfBookApproaches to Measuring Linguistic Differences
oapen.relation.isPartOfBook006438d5-3091-407d-849c-fc2eece18044
oapen.relation.isPartOfBook95cc1f15-2909-4e0a-8bd8-5c515e5efab0
oapen.relation.isFundedBy7292b17b-f01a-4016-94d3-d7fb5ef9fb79
oapen.relation.isFundedByfb214456-da48-4ff7-a1ee-f6407a27f6be
oapen.relation.isbn9783110488081
oapen.collectionEuropean Research Council (ERC)
oapen.place.publicationBerlin/Boston
oapen.grant.number324246
dc.relationisFundedBy7292b17b-f01a-4016-94d3-d7fb5ef9fb79


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