JOURNAL ARTICLE

What can the taxonomy of predicative possession in Malwai Punjabi tell us?: A cognitive linguistics approach.

  • Published In: Cognitive Linguistic Studies, 2024, v. 11, n. 2. P. 370 1 of 3

  • Database: Communication Source 2 of 3

  • Authored By: Lu, Xiaolong 3 of 3

Abstract

The study aims to examine the syntactic and semantic behaviors of predicative possession (i.e., have-possessive constructions) in Malwai Punjabi, an underdocumented dialect within the Indo-Aryan language family. Data were collected from longitudinal online interviews with native speakers as consultants, with audio recordings for transcribed target sentences. The results revealed that all the alienable possession, either permanent/temporary or abstract/concrete, could be marked by the postposition koḷ 'near/with', whereas inalienable possession, such as whole-part relation and kinship, could not be encoded using koḷ. The prototypicality model and schema-based metaphors explained why koḷ was widely used to express alienable possession in Malwai Punjabi. The analysis of companion and proximity schemata also justified the extended semantics of predicative possession, suggesting a metaphorical mapping of accompaniment and location onto possession. From a typological angle, the case study can not only provide further evidence for the existence of split possession but also contribute to a cognitive understanding of predicative possession in relation to other languages. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Cognitive Linguistic Studies. 2024/07, Vol. 11, Issue 2, p370
  • Document Type:Article
  • Subject Area:Language and Linguistics
  • Publication Date:2024
  • ISSN:2213-8722
  • DOI:10.1075/cogls.22002.lu
  • Accession Number:180227107
  • Copyright Statement:Copyright of Cognitive Linguistic Studies is the property of John Benjamins Publishing Co. 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.