@inproceedings{kali-kodner-2022-language,
    title = "Language Acquisition, Neutral Change, and Diachronic Trends in Noun Classifiers",
    author = "Kali, Aniket  and
      Kodner, Jordan",
    editor = "Tahmasebi, Nina  and
      Montariol, Syrielle  and
      Kutuzov, Andrey  and
      Hengchen, Simon  and
      Dubossarsky, Haim  and
      Borin, Lars",
    booktitle = "Proceedings of the 3rd Workshop on Computational Approaches to Historical Language Change",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.lchange-1.2",
    doi = "10.18653/v1/2022.lchange-1.2",
    pages = "11--22",
    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.",
}