A quantitative study of measure words in Mandarin Chinese

This study investigates the quantitative properties of Mandarin measure words using corpus data and word embeddings to address three research questions: (1) how does constructional productivity relate to construction frequency and construction frequency distributions; (2) how semantically cohesive are different measure words; and (3) do classifiers and massifiers differ systematically in productivity and semantic cohesion. We estimated the productivity of measure word constructions with the Good–Turing estimate of unseen types and observed that productivity decreases linearly with the number of types, consistent with previously reported results for Mandarin compounds, and indicating that individual measure word constructions are sampled from the same population of measure word constructions (a measure word schema). The rank–frequency distributions of measure words have power-law like distributions, most of which are best characterized with breakpoint linear regression models. These yield quantitative measures that are predictive for construction productivity. Construction productivity also covaried with semantic measures gauging the semantic cohesion measure word constructions, such that semantically more cohesive measure words were associated with lower Good–Turing estimates of unseen types. Furthermore, massifiers were observed to be more productive and more semantically cohesive than classifiers. Our study demonstrates that the quantitative measures that have proved useful for gauging the productivity of derivation and compounding (morphological constructions) also provide insight into the productivity of measure words (syntactic constructions).