Learning methods to combine linguistic indicators: improving aspectual classification and revealing linguistic insights

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@Article{971886,
  author =       "Eric V. Siegel and Kathleen R. McKeown",
  title =        "Learning methods to combine linguistic indicators:
                 improving aspectual classification and revealing
                 linguistic insights",
  journal =      "Computational Linguistics",
  volume =       "26",
  number =       "4",
  year =         "2000",
  ISSN =         "0891-2017",
  pages =        "595--628",
  DOI =          "doi:10.1162/089120100750105957",
  publisher =    "MIT Press",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://acl.ldc.upenn.edu/J/J00/J00-4004.pdf",
  URL =          "http://citeseer.ist.psu.edu/542166.html",
  size =         "34 pages",
  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

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