This paper describes a method for learning the countability preferences of English nouns from raw text corpora. The method maps the corpus-attested lexico-syntactic properties of each noun onto a feature vector, and uses a suite of memory-based classifiers to predict membership in 4 countability classes. We were able to assign countability to English nouns with a precision of . ence. Knowledge of countability preferences is important both for the analysis and generation of English. In analysis, it helps to constrain the interpretations of parses. .
