Concordance

Query P05-1034 19 >
Sort Right 19 >
Sort Left 19 (608.3 per million)

other,8-2-P05-1034,ak	This method requires a <term> source-language </term><term> dependency parser </term> , <term> target language </term><term> word segmentation </term> and an <term> unsupervised word alignment component </term> .	#8871 This method requires a source-language dependency parser,target language word segmentation and an unsupervised word alignment component.
lr,3-3-P05-1034,ak	We align a <term> parallel corpus </term> , project the <term> source dependency parse </term> onto the <term> target sentence </term> , extract <term> dependency treelet translation pairs </term> , and train a <term> tree-based ordering model </term> .	#8885 We align aparallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model.
tech,36-4-P05-1034,ak	We describe an efficient <term> decoder </term> and show that using these <term> tree-based models </term> in combination with conventional <term> SMT models </term> provides a promising approach that incorporates the power of <term> phrasal SMT </term> with the linguistic generality available in a <term> parser </term> .	#8947 We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in aparser.
other,4-2-P05-1034,ak	This method requires a <term> source-language </term><term> dependency parser </term> , <term> target language </term><term> word segmentation </term> and an <term> unsupervised word alignment component </term> .	#8867 This method requires asource-language dependency parser, target language word segmentation and an unsupervised word alignment component.
model,25-3-P05-1034,ak	We align a <term> parallel corpus </term> , project the <term> source dependency parse </term> onto the <term> target sentence </term> , extract <term> dependency treelet translation pairs </term> , and train a <term> tree-based ordering model </term> .	#8907 We align a parallel corpus, project the source dependency parse onto the target sentence, extract dependency treelet translation pairs, and train atree-based ordering model.
other,14-2-P05-1034,ak	This method requires a <term> source-language </term><term> dependency parser </term> , <term> target language </term><term> word segmentation </term> and an <term> unsupervised word alignment component </term> .	#8877 This method requires a source-language dependency parser, target language word segmentation and anunsupervised word alignment component.
other,11-1-P05-1034,ak	We describe a novel approach to <term> statistical machine translation </term> that combines <term> syntactic information </term> in the <term> source language </term> with recent advances in <term> phrasal translation </term> .	#8850 We describe a novel approach to statistical machine translation that combinessyntactic information in the source language with recent advances in phrasal translation.
model,16-4-P05-1034,ak	We describe an efficient <term> decoder </term> and show that using these <term> tree-based models </term> in combination with conventional <term> SMT models </term> provides a promising approach that incorporates the power of <term> phrasal SMT </term> with the linguistic generality available in a <term> parser </term> .	#8927 We describe an efficient decoder and show that using these tree-based models in combination with conventionalSMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser.
tech,4-4-P05-1034,ak	We describe an efficient <term> decoder </term> and show that using these <term> tree-based models </term> in combination with conventional <term> SMT models </term> provides a promising approach that incorporates the power of <term> phrasal SMT </term> with the linguistic generality available in a <term> parser </term> .	#8915 We describe an efficientdecoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser.
other,17-3-P05-1034,ak	We align a <term> parallel corpus </term> , project the <term> source dependency parse </term> onto the <term> target sentence </term> , extract <term> dependency treelet translation pairs </term> , and train a <term> tree-based ordering model </term> .	#8899 We align a parallel corpus, project the source dependency parse onto the target sentence, extractdependency treelet translation pairs, and train a tree-based ordering model.
tech,21-1-P05-1034,ak	We describe a novel approach to <term> statistical machine translation </term> that combines <term> syntactic information </term> in the <term> source language </term> with recent advances in <term> phrasal translation </term> .	#8860 We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances inphrasal translation.
tech,10-2-P05-1034,ak	This method requires a <term> source-language </term><term> dependency parser </term> , <term> target language </term><term> word segmentation </term> and an <term> unsupervised word alignment component </term> .	#8873 This method requires a source-language dependency parser, target languageword segmentation and an unsupervised word alignment component.
tech,27-4-P05-1034,ak	We describe an efficient <term> decoder </term> and show that using these <term> tree-based models </term> in combination with conventional <term> SMT models </term> provides a promising approach that incorporates the power of <term> phrasal SMT </term> with the linguistic generality available in a <term> parser </term> .	#8938 We describe an efficient decoder and show that using these tree-based models in combination with conventional SMT models provides a promising approach that incorporates the power ofphrasal SMT with the linguistic generality available in a parser.
tech,5-2-P05-1034,ak	This method requires a <term> source-language </term><term> dependency parser </term> , <term> target language </term><term> word segmentation </term> and an <term> unsupervised word alignment component </term> .	#8868 This method requires a source-languagedependency parser, target language word segmentation and an unsupervised word alignment component.
other,15-1-P05-1034,ak	We describe a novel approach to <term> statistical machine translation </term> that combines <term> syntactic information </term> in the <term> source language </term> with recent advances in <term> phrasal translation </term> .	#8854 We describe a novel approach to statistical machine translation that combines syntactic information in thesource language with recent advances in phrasal translation.
other,13-3-P05-1034,ak	We align a <term> parallel corpus </term> , project the <term> source dependency parse </term> onto the <term> target sentence </term> , extract <term> dependency treelet translation pairs </term> , and train a <term> tree-based ordering model </term> .	#8895 We align a parallel corpus, project the source dependency parse onto thetarget sentence, extract dependency treelet translation pairs, and train a tree-based ordering model.
other,8-3-P05-1034,ak	We align a <term> parallel corpus </term> , project the <term> source dependency parse </term> onto the <term> target sentence </term> , extract <term> dependency treelet translation pairs </term> , and train a <term> tree-based ordering model </term> .	#8890 We align a parallel corpus, project thesource dependency parse onto the target sentence, extract dependency treelet translation pairs, and train a tree-based ordering model.
model,10-4-P05-1034,ak	We describe an efficient <term> decoder </term> and show that using these <term> tree-based models </term> in combination with conventional <term> SMT models </term> provides a promising approach that incorporates the power of <term> phrasal SMT </term> with the linguistic generality available in a <term> parser </term> .	#8921 We describe an efficient decoder and show that using thesetree-based models in combination with conventional SMT models provides a promising approach that incorporates the power of phrasal SMT with the linguistic generality available in a parser.
tech,6-1-P05-1034,ak	We describe a novel approach to <term> statistical machine translation </term> that combines <term> syntactic information </term> in the <term> source language </term> with recent advances in <term> phrasal translation </term> .	#8845 We describe a novel approach tostatistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation.


	in Help