Replication Data for: Predicting Russian aspect by frequency across genres

DOI

We ask whether the aspect of individual verbs can be predicted based on the statistical distribution of their inflectional forms and how this is influenced by genre. To address these questions, we present an analysis of the “grammatical profiles” (relative frequency distributions of inflectional forms) of three samples of verbs extracted from the Russian National Corpus, representing three genres: Journalistic prose, Fiction, and Scientific-Technical prose. We find that the aspect of a given verb can be correctly predicted from the distribution of its forms alone with an average accuracy of 92.7%. Remarkably, this accuracy is statistically indistinguishable from the accuracy of prediction of aspect based on morphological marking. We maintain that it would be possible for first language learners to use distributional tendencies, in addition to morphological and other cues (for example semantic and syntactic cues), in acquiring the verbal category of aspect in Russian.

Identifier
DOI https://doi.org/10.18710/BIIGT6
Related Identifier https://www.jstor.org/stable/26633829
Metadata Access https://dataverse.no/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18710/BIIGT6
Provenance
Creator Eckhoff, Hanne; Janda, Laura; Lyashevskaya, Olga Nikolayevna
Publisher DataverseNO
Contributor Eckhoff, Hanne; UiT The Arctic University of Norway; UiT Open Research Data
Publication Year 2017
Rights CC0 1.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/publicdomain/zero/1.0
OpenAccess true
Contact Eckhoff, Hanne (University of Oxford)
Representation
Resource Type Dataset
Format text/plain; type/x-r-syntax; text/tab-separated-values
Size 6898; 2976; 2935; 3568; 5818; 12668; 10682; 4829570; 3378902; 2964955; 10589
Version 1.1
Discipline Humanities