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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,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.
	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,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.
	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.
	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.
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.
	This article considers approaches which rerank the output of an existing <term> probabilistic parser </term> .	#8656 This article considers approaches which rerank the output of an existing probabilistic parser.
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,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.
	The scheme was implemented by gathering <term> statistics </term> on the output of other <term> linguistic tools </term> .	#16664 The scheme was implemented by gathering statistics on the output of other linguistic tools.
tech,3-7-P05-1067,bq	We evaluate the <term> outputs </term> of our <term> MT system </term> using the <term> NIST and Bleu automatic MT evaluation software </term> .	#9512 We evaluate theoutputs of our MT system using the NIST and Bleu automatic MT evaluation software.
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,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.
tech,26-2-N03-1026,bq	Our <term> system </term> incorporates a <term> linguistic parser/generator </term> for <term> LFG </term> , a <term> transfer component </term> for <term> parse reduction </term> operating on <term> packed parse forests </term> , and a <term> maximum-entropy model </term> for <term> stochastic output selection </term> .	#2835 Our system incorporates a linguistic parser/generator for LFG, a transfer component for parse reduction operating on packed parse forests, and a maximum-entropy model for stochastic output selection.
	Second , the <term> sentence-plan-ranker ( SPR ) </term> ranks the list of output <term> sentence plans </term> , and then selects the top-ranked <term> plan </term> .	#1411 Second, the sentence-plan-ranker (SPR) ranks the list of output sentence plans, and then selects the top-ranked plan.
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.
	We consider the case of <term> multi-document summarization </term> , where the input <term> documents </term> are in <term> Arabic </term> , and the output <term> summary </term> is in <term> English </term> .	#7170 We consider the case of multi-document summarization, where the input documents are in Arabic, and the output summary is in English.
	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,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.


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