A plethora of methods for learning English countability

Authors:
Timothy Baldwin;Francis Bond
Affiliations:
Stanford University, Stanford, CA;Nippon Telegraph and Telephone Corporation, Kyoto, Japan
Venue:
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Year:
2003

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Abstract

This paper compares a range of methods for classifying words based on linguistic diagnostics, focusing on the task of learning countabilities for English nouns. We propose two basic approaches to feature representation: distribution-based representation, which simply looks at the distribution of features in the corpus data, and agreement-based representation which analyses the level of token-wise agreement between multiple preprocessor systems. We additionally compare a single multiclass classifier architecture with a suite of binary classifiers, and combine analyses from multiple preprocessors. Finally, we present and evaluate a feature selection method.