Automatically Classifying English Verb-Particle Constructions by Particle Semantics. Cook, P. Master's thesis, Department of Computer Science, University of Toronto, August, 2006.
abstract   bibtex   
We address the issue of automatically determining the semantic contribution of the particle in a verb-particle construction (VPC), a task which has been previously ignored in computational work on VPCs. Adopting a cognitive linguistic standpoint, we assume that every VPC is compositional, and that the semantic contribution of a particle corresponds to one of a small number of senses. We develop a feature space based on syntactic and semantic properties of verbs and VPCs for type classification of English VPCs according to the sense contributed by their particle. We focus on VPCs using the particle up since it is very frequent and exhibits a wide range of meanings. In our experiments on unseen test VPCs, features which are motivated by properties specific to verbs and VPCs outperform linguistically uninformed word co-occurrence features, and give a reduction in error rate of around 20-30% over a chance baseline.
@MastersThesis{	  cook1,
  author	= {Paul Cook},
  title		= {Automatically Classifying English Verb-Particle
		  Constructions by Particle Semantics},
  school	= {Department of Computer Science, University of Toronto},
  month		= {August},
  year		= {2006},
  abstract	= {We address the issue of automatically determining the
		  semantic contribution of the particle in a verb-particle
		  construction (VPC), a task which has been previously
		  ignored in computational work on VPCs. Adopting a cognitive
		  linguistic standpoint, we assume that every VPC is
		  compositional, and that the semantic contribution of a
		  particle corresponds to one of a small number of senses. We
		  develop a feature space based on syntactic and semantic
		  properties of verbs and VPCs for type classification of
		  English VPCs according to the sense contributed by their
		  particle. We focus on VPCs using the particle <i>up</i>
		  since it is very frequent and exhibits a wide range of
		  meanings. In our experiments on unseen test VPCs, features
		  which are motivated by properties specific to verbs and
		  VPCs outperform linguistically uninformed word
		  co-occurrence features, and give a reduction in error rate
		  of around 20-30% over a chance baseline.},
  download	= {http://www.cs.toronto.edu/~pcook/Cook2006.pdf}
}
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