| other,16-6-H01-1042,bq | experiment using <term> machine translation | output | </term> . Subjects were given a set of up | #680 We tested this to see if similar criteria could be elicited from duplicating the experiment using machine translation output. |
| . 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. | |
| other,9-6-E06-1035,bq | transcription errors </term> inevitable in <term> ASR | output | </term> have a negative impact on models | #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,16-2-N03-1018,bq | on <term> post-processing </term> the <term> | output | </term> of black-box <term> OCR systems </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. |
| </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. | |
| 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. | |
| 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. |
| 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,15-5-N03-1026,bq | <term> grammaticality </term> of the <term> system | output | </term> due to the use of a <term> constraint-based | #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. |
| </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. | |
| 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,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,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. |
| other,16-3-H01-1042,bq | the <term> intelligibility </term> of <term> MT | output | </term> . A <term> language learning experiment | #626 This, the first experiment in a series of experiments, looks at the intelligibility of MT output. |
| 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. |
| determine whether they believed the sample | output | to be an <term> expert human translation </term> | #727 The subjects were given three minutes per extract to determine whether they believed the sample output to be an expert human translation or a machine translation. | |
| 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. |
| 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. | |
| 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. |
