Web and Corpus Methods for Malay Count Classifier Prediction

We examine the capacity of Web and corpus frequency methods to predict preferred count classifiers for nouns in Malay. The observed F-score for the Web model of 0.671 considerably outperformed corpus-based frequency and machine learning models. We expect that this is a fruitful extension for Web–as–corpus approaches to lexicons in languages other than English, but further research is required in other South-East and East Asian languages.