Bad Data can be Good Data – the Significance of Different Methods for Syntactic Theorizing

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dc.contributor.author Rehn, Alexandra
dc.contributor.author Brandner, Ellen
dc.date.accessioned 2022-12-21T13:54:22Z
dc.date.available 2022-12-21T13:54:22Z
dc.date.issued 2022-12-21
dc.identifier.uri http://hdl.handle.net/10900/134551
dc.identifier.uri http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-1345517 de_DE
dc.identifier.uri http://dx.doi.org/10.15496/publikation-75902
dc.description.abstract Contribution to Linguistic Evidence 2020 en
dc.language.iso en de_DE
dc.publisher Universität Tübingen de_DE
dc.rights ubt-podok de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=de de_DE
dc.rights.uri http://tobias-lib.uni-tuebingen.de/doku/lic_mit_pod.php?la=en en
dc.subject.ddc 400 de_DE
dc.subject.other method comparison en
dc.subject.other regional variation en
dc.subject.other syntactic variation en
dc.subject.other dialect en
dc.title Bad Data can be Good Data – the Significance of Different Methods for Syntactic Theorizing en
dc.type ConferencePaper de_DE
utue.publikation.fachbereich Philosophische Fakultät de_DE
utue.publikation.fakultaet 5 Philosophische Fakultät de_DE
utue.opus.portal ProcLingEvi2020 de_DE
utue.publikation.noppn yes de_DE

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