Concordance

Query H01-1042 24 (1,118.9 per million)

tech,12-1-H01-1042,bq	The purpose of this research is to test the efficacy of applying <term> automated evaluation techniques </term> , originally devised for the <term> evaluation </term> of <term> human language learners </term> , to the <term> output </term> of <term> machine translation ( MT ) systems </term> .	#556 The purpose of this research is to test the efficacy of applyingautomated evaluation techniques, originally devised for the evaluation of human language learners, to the output of machine translation (MT) systems.
tech,20-1-H01-1042,bq	The purpose of this research is to test the efficacy of applying <term> automated evaluation techniques </term> , originally devised for the <term> evaluation </term> of <term> human language learners </term> , to the <term> output </term> of <term> machine translation ( MT ) systems </term> .	#564 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for theevaluation of human language learners, to the output of machine translation (MT) systems.
other,22-1-H01-1042,bq	The purpose of this research is to test the efficacy of applying <term> automated evaluation techniques </term> , originally devised for the <term> evaluation </term> of <term> human language learners </term> , to the <term> output </term> of <term> machine translation ( MT ) systems </term> .	#566 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation ofhuman language learners, to the output of machine translation (MT) systems.
other,28-1-H01-1042,bq	The purpose of this research is to test the efficacy of applying <term> automated evaluation techniques </term> , originally devised for the <term> evaluation </term> of <term> human language learners </term> , to the <term> output </term> of <term> machine translation ( MT ) systems </term> .	#572 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation of human language learners, to theoutput of machine translation (MT) systems.
tech,30-1-H01-1042,bq	The purpose of this research is to test the efficacy of applying <term> automated evaluation techniques </term> , originally devised for the <term> evaluation </term> of <term> human language learners </term> , to the <term> output </term> of <term> machine translation ( MT ) systems </term> .	#574 The purpose of this research is to test the efficacy of applying automated evaluation techniques, originally devised for the evaluation of human language learners, to the output ofmachine translation ( MT ) systems.
tech,4-2-H01-1042,bq	We believe that these <term> evaluation techniques </term> will provide information about both the <term> human language learning process </term> , the <term> translation process </term> and the <term> development </term> of <term> machine translation systems </term> .	#585 We believe that theseevaluation techniques will provide information about both the human language learning process, the translation process and the development of machine translation systems.
other,12-2-H01-1042,bq	We believe that these <term> evaluation techniques </term> will provide information about both the <term> human language learning process </term> , the <term> translation process </term> and the <term> development </term> of <term> machine translation systems </term> .	#593 We believe that these evaluation techniques will provide information about both thehuman language learning process, the translation process and the development of machine translation systems.
other,18-2-H01-1042,bq	We believe that these <term> evaluation techniques </term> will provide information about both the <term> human language learning process </term> , the <term> translation process </term> and the <term> development </term> of <term> machine translation systems </term> .	#599 We believe that these evaluation techniques will provide information about both the human language learning process, thetranslation process and the development of machine translation systems.
tech,22-2-H01-1042,bq	We believe that these <term> evaluation techniques </term> will provide information about both the <term> human language learning process </term> , the <term> translation process </term> and the <term> development </term> of <term> machine translation systems </term> .	#603 We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and thedevelopment of machine translation systems.
tech,24-2-H01-1042,bq	We believe that these <term> evaluation techniques </term> will provide information about both the <term> human language learning process </term> , the <term> translation process </term> and the <term> development </term> of <term> machine translation systems </term> .	#605 We believe that these evaluation techniques will provide information about both the human language learning process, the translation process and the development ofmachine translation systems.
other,14-3-H01-1042,bq	This , the first experiment in a series of experiments , looks at the <term> intelligibility </term> of <term> MT output </term> .	#623 This, the first experiment in a series of experiments, looks at theintelligibility of MT output.
other,16-3-H01-1042,bq	This , the first experiment in a series of experiments , looks at the <term> intelligibility </term> of <term> MT output </term> .	#625 This, the first experiment in a series of experiments, looks at the intelligibility ofMT output.
other,1-4-H01-1042,bq	A <term> language learning experiment </term> showed that <term> assessors </term> can differentiate <term> native from non-native language essays </term> in less than 100 <term> words </term> .	#629 Alanguage learning experiment showed that assessors can differentiate native from non-native language essays in less than 100 words.
other,6-4-H01-1042,bq	A <term> language learning experiment </term> showed that <term> assessors </term> can differentiate <term> native from non-native language essays </term> in less than 100 <term> words </term> .	#634 A language learning experiment showed thatassessors can differentiate native from non-native language essays in less than 100 words.
other,9-4-H01-1042,bq	A <term> language learning experiment </term> showed that <term> assessors </term> can differentiate <term> native from non-native language essays </term> in less than 100 <term> words </term> .	#637 A language learning experiment showed that assessors can differentiatenative from non-native language essays in less than 100 words.
other,18-4-H01-1042,bq	A <term> language learning experiment </term> showed that <term> assessors </term> can differentiate <term> native from non-native language essays </term> in less than 100 <term> words </term> .	#646 A language learning experiment showed that assessors can differentiate native from non-native language essays in less than 100words.
other,9-5-H01-1042,bq	Even more illuminating was the factors on which the <term> assessors </term> made their decisions .	#657 Even more illuminating was the factors on which theassessors made their decisions.
other,16-6-H01-1042,bq	We tested this to see if similar criteria could be elicited from duplicating the experiment using <term> machine translation output </term> .	#678 We tested this to see if similar criteria could be elicited from duplicating the experiment usingmachine translation output.
other,11-7-H01-1042,bq	Subjects were given a set of up to six extracts of <term> translated newswire text </term> .	#693 Subjects were given a set of up to six extracts oftranslated newswire text.
other,5-8-H01-1042,bq	Some of the extracts were <term> expert human translations </term> , others were <term> machine translation outputs </term> .	#702 Some of the extracts wereexpert human translations, others were machine translation outputs.


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