How Much Data Does Linguistic Theory Need? On the Tolerance Principle of Linguistic Theorizing

[摘要] Yang’s (2016) Tolerance Principle describes with incredible precision how many exceptions the mechanisms of child language acquisition can tolerate to induce a productive rule, and is a notable advance in the long-standing controversy as to the amount of data necessary for the acquisition of language. The present contribution addresses a different but related issue, that of the amount of data on variation in languages needed by a linguist to develop a theory of language. Using as a model the perennial question of how many languages should be considered to formulate a general theory of language, I will show that discussions about the type and amount of data needed for linguistic theorising cannot be fruitful without taking into account the type of linguistic theory and its goals. Moreover, the type of linguistic theory itself depends on the way in which the object of study is conceived. I propose that the two main types of current linguistic theory (functionalism and formalism) correlate broadly to different scientific methods: the inductive one (which proceeds from languages to language) and the deductive one (which proceeds from language to languages), respectively. My aim is to show that the type of data that can falsify a certain linguistic theory is different depending on whether the theory is deductive or inductive. That is, the two types of theory have a different “tolerance threshold” regarding the sparseness of data. Hence, the expectation of progress that new sources of data on language variation can provide for linguistic theory should be modulated according to the objectives and assumptions of each language theory.

[学科分类] 计算机网络和通讯

[关键词] Linguistic theories;big data;Language variation;Language typology;Inductive model;Deductive model