JOURNAL ARTICLE
Split Reduplicant Hypothesis: Evidence from tetrasyllabic reduplicated adjectives in Taiwanese.
Published In: International Journal of Chinese Linguistics, 2025, v. 12, n. 1. P. 66 1 of 3
Database: Communication Source 2 of 3
Authored By: Cheng, Adæmrys Chihjen 3 of 3
Abstract
In this paper, I propose Split Reduplicant Hypothesis against previous research, claiming of one reduplicant being able to yield the diverse interpretation of adjectival reduplications, specifically the tetrasyllabic reduplicated adjectives. Following cartographic syntax, it is not satisfying and problematic for one reduplicant to host the diverse features and denote the distinct interpretations. Therefore, three reduplicants are proposed: redh, redm as well as redl, for example, redh refers to [aug], an emphatic interpretation; redm refers to [dim], a weakening reading; and redl refers to [neu], a positive interpretation, respectively. In other words, I propose the hierarchical structure of split reduplicants based on the interactions with the Taiwanese reduplicated adjectives. In addition to morphosyntactic reduplication, pragmatic-driven reduplication is also found. From the cross-linguistic point of view, Split Reduplicant Hypothesis is also applicable across languages. Intriguingly, tone sandhi also can play a role in determining the interpretations of adjectival reduplications, which indicates tone sandhi can correspond to the distinct reduplicants. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:International Journal of Chinese Linguistics. 2025/01, Vol. 12, Issue 1, p66
- Document Type:Article
- Subject Area:Literature and Writing
- Publication Date:2025
- ISSN:2213-8706
- DOI:10.1075/ijchl.00035.che
- Accession Number:185860490
- Copyright Statement:Copyright of International Journal of Chinese Linguistics 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.)
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