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Learning Context-Dependent Mappings from Sentences to Logical Form Luke S. Zettlemoyer and Michael Collins
 

Summary: Learning Context-Dependent Mappings from Sentences to Logical Form
Luke S. Zettlemoyer and Michael Collins
MIT CSAIL
Cambridge, MA 02139
{lsz,mcollins}@csail.mit.com
Abstract
We consider the problem of learning
context-dependent mappings from sen-
tences to logical form. The training ex-
amples are sequences of sentences anno-
tated with lambda-calculus meaning rep-
resentations. We develop an algorithm that
maintains explicit, lambda-calculus repre-
sentations of salient discourse entities and
uses a context-dependent analysis pipeline
to recover logical forms. The method uses
a hidden-variable variant of the percep-
tion algorithm to learn a linear model used
to select the best analysis. Experiments
on context-dependent utterances from the

  

Source: Anderson, Richard - Department of Computer Science and Engineering, University of Washington at Seattle
Collins, Michael - Computer Science and Artificial Intelligence Laboratory & Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (MIT)
Columbia University, Department of Computer Science, Languages and Compilers Research Group

 

Collections: Computer Technologies and Information Sciences