| other,11-3-I05-6011,bq | </term> and improving <term> machine translation | outputs | </term> . Annotating <term> honorifics </term> | #8614 This referential information is vital for resolving zero pronouns and improving machine translation outputs. |
| other,30-2-H05-2007,bq | patterns </term> in <term> machine translation | output | </term> . Automatic <term> evaluation metrics | #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. |
| </term> of a <term> parser </term> 's multiple | output | . Some examples of <term> paraphrasing </term> | #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 | <term> performance </term> of using <term> ASR | output | </term> as opposed to <term> human transcription | #10519 We then explore the impact on performance of using ASR output as opposed to human transcription. |
| the <term> shared derivation forest </term> | output | by a prior <term> RCL parser </term> for a | #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 ) | #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 | <term> methods </term> for <term> scoring system | output | </term> is an impediment to progress in the | #7578 The lack of automatic methods for scoring system output is an impediment to progress in the field, which we address with this work. |
| article considers approaches which rerank the | output | of an existing <term> probabilistic parser | #8656 This article considers approaches which rerank the output of an existing probabilistic parser. | |
| other,38-1-N03-1018,bq | through its transformation into the <term> noisy | output | </term> of an <term> OCR system </term> . The | #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 | human language learners </term> , to the <term> | output | </term> of <term> machine translation ( MT | #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. |
| 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 | <term> model </term> . We evaluate the <term> | outputs | </term> of our <term> MT system </term> using | #9512 We evaluate theoutputs of our MT system using the NIST and Bleu automatic MT evaluation software. |
| other,21-3-N03-1001,bq | particular <term> domain </term> ; the <term> | output | </term> of <term> recognition </term> with this | #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 | pipeline </term> that capitalizes on <term> | output | quality </term> . The demonstrator embodies | #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 | maximum-entropy model </term> for <term> stochastic | output | selection </term> . Furthermore , we propose | #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. |
| sentence-plan-ranker ( SPR ) </term> ranks the list of | output | <term> sentence plans </term> , and then selects | #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 | system </term> produces the <term> translation | output | </term> sufficient for content understanding | #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. |
| </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. | |
| directly compare commercial <term> systems </term> | outputting | <term> unsegmented texts </term> with , for | #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 | </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. |
