A plethora of methods for learning English countability

• Main Authors: Baldwin, Timothy, Bond, Francis
• Other Authors: School of Humanities and Social Sciences
• Format: Conference or Workshop Item
• Language: English
• Published: 2011
• Subjects: DRNTU::Humanities::Linguistics::Sociolinguistics::Computational linguistics
• Online Access: https://hdl.handle.net/10356/93871; http://hdl.handle.net/10220/6822
• Institution: Nanyang Technological University

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 tokenwise 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.