Learning methods to combine linguistic indicators: improving aspectual classification and revealing linguistic insights
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- @Article{971886,
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author = "Eric V. Siegel and Kathleen R. McKeown",
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title = "Learning methods to combine linguistic indicators:
improving aspectual classification and revealing
linguistic insights",
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journal = "Computational Linguistics",
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volume = "26",
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number = "4",
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year = "2000",
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ISSN = "0891-2017",
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pages = "595--628",
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DOI = "doi:10.1162/089120100750105957",
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publisher = "MIT Press",
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keywords = "genetic algorithms, genetic programming",
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URL = "http://acl.ldc.upenn.edu/J/J00/J00-4004.pdf",
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URL = "http://citeseer.ist.psu.edu/542166.html",
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size = "34 pages",
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abstract = "Aspectual classification maps verbs to a small set of
primitive categories in order to reason about time.
This classification is necessary for interpreting
temporal modifiers and assessing temporal
relationships, and is therefore a required component
for many natural language applications. A verb's
aspectual category can be predicted by co-occurrence
frequencies between the verb and certain linguistic
modifiers. These frequency measures, called linguistic
indicators, are chosen by linguistic insights.
However,linguistic indicators used in isolation are
predictively incomplete, and are therefore insufficient
when used individually. In this article, we compare
three supervised machine learning methods for combining
multiple linguistic indicators for aspectual
classification:decision trees, genetic programming, and
logistic regression. A set of 14 indicators are
combined for classification according to two aspectual
distinctions. This approach improves the classification
performance for both distinctions, as evaluated over
unrestricted sets of verbs occurring across two
corpora. This demonstrates the effectiveness of the
linguistic indicators and provides a much-needed
full-scale method for automatic aspectual
classification. Moreover, the models resulting from
learning reveal several linguistic insights that are
relevant to aspectual classification. We also compare
supervised learning methods with an unsupervised method
for this task.",
- }
Genetic Programming entries for
Eric Siegel
Kathleen R McKeown
Citations