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A Latent Dirichlet Allocation method for Selectional Preferences Alan Ritter, Mausam and Oren Etzioni
 

Summary: A Latent Dirichlet Allocation method for Selectional Preferences
Alan Ritter, Mausam and Oren Etzioni
Department of Computer Science and Engineering
Box 352350, University of Washington, Seattle, WA 98195, USA
{aritter,mausam,etzioni}@cs.washington.edu
Abstract
The computation of selectional prefer-
ences, the admissible argument values for
a relation, is a well-known NLP task with
broad applicability. We present LDA-SP,
which utilizes LinkLDA (Erosheva et al.,
2004) to model selectional preferences.
By simultaneously inferring latent top-
ics and topic distributions over relations,
LDA-SP combines the benefits of pre-
vious approaches: like traditional class-
based approaches, it produces human-
interpretable classes describing each re-
lation's preferences, but it is competitive
with non-class-based methods in predic-

  

Source: Anderson, Richard - Department of Computer Science and Engineering, University of Washington at Seattle
Mausam - Department of Computer Science and Engineering, University of Washington at Seattle

 

Collections: Computer Technologies and Information Sciences