PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Sentence Compression as Tree Transduction
Trevor Cohn and Mirella Lapata
Journal of Artificial Intelligence Research Volume 34, pp. 637-674, 2009. ISSN 1076-9757

Abstract

This paper presents a tree-to-tree transduction method for sentence compression. Our model is based on synchronous tree substitution grammar, a formalism that allows local distortion of the tree topology and can thus naturally capture structural mismatches. We describe an algorithm for decoding in this framework and show how the model can be trained discriminatively within a large margin framework. Experimental results on sentence compression bring significant improvements over a state-of-the-art model.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:5887
Deposited By:Trevor Cohn
Deposited On:08 March 2010