  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.
