JOURNAL ARTICLE

Studying negative evidence in Finnish language corpora.

  • Published In: Word Structure, 2023, v. 16, n. 2/3. P. 206 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Nikolaev, Alexandre; Bermel, Neil 3 of 3

Abstract

This study explores the relationship between lower-than-expected frequencies of word forms and inherent gaps in Finnish inflectional paradigms. The research aims to determine whether it is possible to predict paradigmatic gaps from lower-than-expected frequencies of word forms. We examined Finnish nouns inflected in a marginal case (the instructive) and hypothesized that some of these nouns may potentially have gaps in their inflectional paradigms. However, we found that such gaps are contingent and do not cause uncertainty when filled. We find that the correlation between inherent gaps and lower frequencies is one-directional: predicting inherent gaps from lower-than-expected frequencies is problematic. The results suggest that any paradigmatic gap suggested by corpus frequency is more likely to be contingent than inherent, and that the less semantic need there is for a particular word form, the more likely it will be unattested even in a large corpus. The research highlights the importance of considering semantic profiles when analyzing the grammaticality of word forms and suggests that statistical tests like Fisher's exact are not necessarily the right approach to tackle the problem of negative evidence in corpus studies. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Word Structure. 2023/11, Vol. 16, Issue 2/3, p206
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2023
  • ISSN:1750-1245
  • DOI:10.3366/word.2023.0229
  • Accession Number:173514422
  • Copyright Statement:Copyright of Word Structure is the property of Edinburgh University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.