On the Idiosyncrasies of the Mandarin Chinese Classifier System
This work addresses a gap in linguistics by providing a statistical quantification of classifier idiosyncrasies, though it is incremental as it builds on prior linguistic studies.
The paper tackled the problem of quantifying idiosyncrasies in the Mandarin Chinese classifier system by introducing an information-theoretic approach to measure how much semantic information about nouns reduces uncertainty in classifier choice, finding that shape nouns reduce uncertainty the most.
While idiosyncrasies of the Chinese classifier system have been a richly studied topic among linguists (Adams and Conklin, 1973; Erbaugh, 1986; Lakoff, 1986), not much work has been done to quantify them with statistical methods. In this paper, we introduce an information-theoretic approach to measuring idiosyncrasy; we examine how much the uncertainty in Mandarin Chinese classifiers can be reduced by knowing semantic information about the nouns that the classifiers modify. Using the empirical distribution of classifiers from the parsed Chinese Gigaword corpus (Graff et al., 2005), we compute the mutual information (in bits) between the distribution over classifiers and distributions over other linguistic quantities. We investigate whether semantic classes of nouns and adjectives differ in how much they reduce uncertainty in classifier choice, and find that it is not fully idiosyncratic; while there are no obvious trends for the majority of semantic classes, shape nouns reduce uncertainty in classifier choice the most.