Language Acquisition, Neutral Change, and Diachronic Trends in Noun Classifiers

Aniket Kali, Jordan Kodner


Abstract
Languages around the world employ classifier systems as a method of semantic organization and categorization. These systems are rife with variability, violability, and ambiguity, and are prone to constant change over time. We explicitly model change in classifier systems as the population-level outcome of child language acquisition over time in order to shed light on the factors that drive change to classifier systems. Our research consists of two parts: a contrastive corpus study of Cantonese and Mandarin child-directed speech to determine the role that ambiguity and homophony avoidance may play in classifier learning and change followed by a series of population-level learning simulations of an abstract classifier system. We find that acquisition without reference to ambiguity avoidance is sufficient to drive broad trends in classifier change and suggest an additional role for adults and discourse factors in classifier death.
Anthology ID:
2022.lchange-1.2
Volume:
Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venues:
ACL | LChange
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–22
Language:
URL:
https://aclanthology.org/2022.lchange-1.2
DOI:
10.18653/v1/2022.lchange-1.2
Bibkey:
Cite (ACL):
Aniket Kali and Jordan Kodner. 2022. Language Acquisition, Neutral Change, and Diachronic Trends in Noun Classifiers. In Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change, pages 11–22, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
Language Acquisition, Neutral Change, and Diachronic Trends in Noun Classifiers (Kali & Kodner, LChange 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lchange-1.2.pdf
Code
 an-k45/classifier-change