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A plethora of methods for learning English countability (2003)
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| Venue: | In Proc. of the 2003 Conference on Empirical Methods in Natural Language Processing (EMNLP 2003 |
| Citations: | 16 - 6 self |
BibTeX
@INPROCEEDINGS{Baldwin03aplethora,
author = {Timothy Baldwin},
title = {A plethora of methods for learning English countability},
booktitle = {In Proc. of the 2003 Conference on Empirical Methods in Natural Language Processing (EMNLP 2003},
year = {2003},
pages = {73--80}
}
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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 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. 1
Keyphrases
english countability multiple preprocessors corpus data basic approach feature selection method linguistic diagnostics tokenwise agreement distribution-based representation multiple preprocessor system binary classifier single multiclass classifier architecture agreement-based representation english noun
