Concordance

Query output 28 >
Shuffle 28 (1,305.4 per million)

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> .	#680 We tested this to see if similar criteria could be elicited from duplicating the experiment using machine translation output.
other,38-1-N03-1018,bq	In this paper , we introduce a <term> generative probabilistic optical character recognition ( OCR ) model </term> that describes an end-to-end process in the <term> noisy channel framework </term> , progressing from generation of <term> true text </term> through its transformation into the <term> noisy output </term> of an <term> OCR system </term> .	#2706 In this paper, we introduce a generative probabilistic optical character recognition (OCR) model that describes an end-to-end process in the noisy channel framework, progressing from generation of true text through its transformation into the noisy output of an OCR system.
other,16-2-N03-1018,bq	The <term> model </term> is designed for use in <term> error correction </term> , with a focus on <term> post-processing </term> the <term> output </term> of black-box <term> OCR systems </term> in order to make it more useful for <term> NLP tasks </term> .	#2728 The model is designed for use in error correction, with a focus on post-processing theoutput of black-box OCR systems in order to make it more useful for NLP tasks.
measure(ment),6-3-H05-1117,bq	The lack of automatic <term> methods </term> for <term> scoring system output </term> is an impediment to progress in the field , which we address with this work .	#7578 The lack of automatic methods for scoring system output is an impediment to progress in the field, which we address with this work.
other,9-6-E06-1035,bq	We also find that the <term> transcription errors </term> inevitable in <term> ASR output </term> have a negative impact on models that combine <term> lexical-cohesion and conversational features </term> , but do not change the general preference of approach for the two tasks .	#10618 We also find that the transcription errors inevitable in ASR output have a negative impact on models that combine lexical-cohesion and conversational features, but do not change the general preference of approach for the two tasks.
other,30-2-H05-2007,bq	We incorporate this analysis into a <term> diagnostic tool </term> intended for <term> developers </term> of <term> machine translation systems </term> , and demonstrate how our application can be used by <term> developers </term> to explore <term> patterns </term> in <term> machine translation output </term> .	#7676 We incorporate this analysis into a diagnostic tool intended for developers of machine translation systems, and demonstrate how our application can be used by developers to explore patterns in machine translation output.
	The use of <term> BLEU </term> at the <term> character </term> level eliminates the <term> word segmentation problem </term> : it makes it possible to directly compare commercial <term> systems </term> outputting <term> unsegmented texts </term> with , for instance , <term> statistical MT systems </term> which usually segment their <term> outputs </term> .	#7769 The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systemsoutputting unsegmented texts with, for instance, statistical MT systems which usually segment their outputs.
other,18-6-H01-1041,bq	Having been trained on <term> Korean newspaper articles </term> on missiles and chemical biological warfare , the <term> system </term> produces the <term> translation output </term> sufficient for content understanding of the <term> original document </term> .	#534 Having been trained on Korean newspaper articles on missiles and chemical biological warfare, the system produces the translation output sufficient for content understanding of the original document.
	This paper presents a new <term> interactive disambiguation scheme </term> based on the <term> paraphrasing </term> of a <term> parser </term> 's multiple output .	#15726 This paper presents a new interactive disambiguation scheme based on the paraphrasing of a parser's multiple output.
other,21-3-N03-1001,bq	In our method , <term> unsupervised training </term> is first used to train a <term> phone n-gram model </term> for a particular <term> domain </term> ; the <term> output </term> of <term> recognition </term> with this <term> model </term> is then passed to a <term> phone-string classifier </term> .	#2276 In our method, unsupervised training is first used to train a phone n-gram model for a particular domain; theoutput of recognition with this model is then passed to a phone-string classifier.
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> .	#626 This, the first experiment in a series of experiments, looks at the intelligibility of MT output.
other,12-4-P05-2016,bq	We also refer to an <term> evaluation method </term> and plan to compare our <term> system 's output </term> with a <term> benchmark system </term> .	#9825 We also refer to an evaluation method and plan to compare our system's output with a benchmark system.
other,11-3-I05-6011,bq	This <term> referential information </term> is vital for resolving <term> zero pronouns </term> and improving <term> machine translation outputs </term> .	#8614 This referential information is vital for resolving zero pronouns and improving machine translation outputs.
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.
	In this paper we show how two standard outputs from <term> information extraction ( IE ) systems </term> - <term> named entity annotations </term> and <term> scenario templates </term> - can be used to enhance access to <term> text collections </term> via a standard <term> text browser </term> .	#282 In this paper we show how two standard outputs from information extraction (IE) systems - named entity annotations and scenario templates - can be used to enhance access to text collections via a standard text browser.
other,9-4-E06-1035,bq	We then explore the impact on <term> performance </term> of using <term> ASR output </term> as opposed to <term> human transcription </term> .	#10519 We then explore the impact on performance of using ASR output as opposed to human transcription.
other,18-1-P06-4014,bq	The <term> LOGON MT demonstrator </term> assembles independently valuable <term> general-purpose NLP components </term> into a <term> machine translation pipeline </term> that capitalizes on <term> output quality </term> .	#11807 The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes onoutput quality.
other,38-4-I05-2014,bq	The use of <term> BLEU </term> at the <term> character </term> level eliminates the <term> word segmentation problem </term> : it makes it possible to directly compare commercial <term> systems </term> outputting <term> unsegmented texts </term> with , for instance , <term> statistical MT systems </term> which usually segment their <term> outputs </term> .	#7784 The use of BLEU at the character level eliminates the word segmentation problem: it makes it possible to directly compare commercial systems outputting unsegmented texts with, for instance, statistical MT systems which usually segment theiroutputs.
other,15-5-N03-1026,bq	Overall <term> summarization </term> quality of the proposed <term> system </term> is state-of-the-art , with guaranteed <term> grammaticality </term> of the <term> system output </term> due to the use of a <term> constraint-based parser/generator </term> .	#2899 Overall summarization quality of the proposed system is state-of-the-art, with guaranteed grammaticality of the system output due to the use of a constraint-based parser/generator.
	The <term> non-deterministic parsing choices </term> of the <term> main parser </term> for a <term> language L </term> are directed by a <term> guide </term> which uses the <term> shared derivation forest </term> output by a prior <term> RCL parser </term> for a suitable <term> superset of L.	#1723 The non-deterministic parsing choices of the main parser for a language L are directed by a guide which uses the shared derivation forestoutput by a prior RCL parser for a suitable superset of L.


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