Title
Linguistically informed statistical models of constituent structure for ordering in sentence realization
Abstract
We present several statistical models of syntactic constituent order for sentence realization. We compare several models, including simple joint models inspired by existing statistical parsing models, and several novel conditional models. The conditional models leverage a large set of linguistic features without manual feature selection. We apply and evaluate the models in sentence realization for French and German and find that a particular conditional model outperforms all others. We employ a version of that model in an evaluation on unordered trees from the Penn TreeBank. We offer this result on standard data as a reference-point for evaluations of ordering in sentence realization.
Year
DOI
Venue
2004
10.3115/1220355.1220452
COLING
Keywords
Field
DocType
linguistic feature,simple joint model,statistical parsing model,sentence realization,large set,penn treebank,statistical model,constituent structure,particular conditional model,novel conditional model,conditional model,feature selection
Feature selection,Pattern recognition,Computer science,Artificial intelligence,Natural language processing,Treebank,Statistical model,Statistical parsing,Syntax,Discriminative model,Sentence,German
Conference
Volume
Citations 
PageRank 
C04-1
24
1.53
References 
Authors
14
6
Name
Order
Citations
PageRank
Eric Ringger132821.57
Michael Gamon2312.13
Robert C. Moore32432646.93
David Rojas4241.53
Martine Smets510010.09
Simon Corston-Oliver634925.25