A plethora of methods for learning English countability
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, whi...
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| Main Authors: | , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2011
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/93871 http://hdl.handle.net/10220/6822 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
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