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 94.6%.
REF for example describe a classification system for the acquisition of countability preferences for English nouns from corpora based on 4 countability categories.
Learning The Countability Of English Nouns From Corpus Data
Annual Meeting Of The Association For Computational Linguistics
Computer Science