tech,10-1-H01-1041,bq At MIT Lincoln Laboratory , we have been developing a <term> Korean-to-English machine translation system </term><term> CCLINC ( Common Coalition Language System at Lincoln Laboratory ) </term> .
tool,1-2-H01-1041,bq The <term> CCLINC Korean-to-English translation system </term> consists of two <term> core modules </term> , <term> language understanding and generation modules </term> mediated by a <term> language neutral meaning representation </term> called a <term> semantic frame </term> .
tech,5-4-H01-1041,bq ( ii ) High quality <term> translation </term> via <term> word sense disambiguation </term> and accurate <term> word order generation </term> of the <term> target language </term> .
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> .
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> .
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> .
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> .
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> .
other,5-8-H01-1042,bq Some of the extracts were <term> expert human translations </term> , others were <term> machine translation outputs </term> .
other,11-8-H01-1042,bq Some of the extracts were <term> expert human translations </term> , others were <term> machine translation outputs </term> .
other,19-9-H01-1042,bq The subjects were given three minutes per extract to determine whether they believed the sample output to be an <term> expert human translation </term> or a <term> machine translation </term> .
other,24-9-H01-1042,bq The subjects were given three minutes per extract to determine whether they believed the sample output to be an <term> expert human translation </term> or a <term> machine translation </term> .
tech,23-1-P01-1004,bq In this paper , we compare the relative effects of <term> segment order </term> , <term> segmentation </term> and <term> segment contiguity </term> on the <term> retrieval performance </term> of a <term> translation memory system </term> .
tech,4-3-P01-1007,bq For example , after <term> translation </term> into an equivalent <term> RCG </term> , any <term> tree adjoining grammar </term> can be parsed in <term> O ( n6 ) time </term> .
lr,12-2-P01-1008,bq We present an <term> unsupervised learning algorithm </term> for <term> identification of paraphrases </term> from a <term> corpus of multiple English translations </term> of the same <term> source text </term> .
model,4-1-N03-1017,bq We propose a new <term> phrase-based translation model </term> and <term> decoding algorithm </term> that enables us to evaluate and compare several , previously proposed <term> phrase-based translation models </term> .
model,21-1-N03-1017,bq We propose a new <term> phrase-based translation model </term> and <term> decoding algorithm </term> that enables us to evaluate and compare several , previously proposed <term> phrase-based translation models </term> .
other,30-3-N03-1017,bq Our empirical results , which hold for all examined <term> language pairs </term> , suggest that the highest levels of performance can be obtained through relatively simple means : <term> heuristic learning </term> of <term> phrase translations </term> from <term> word-based alignments </term> and <term> lexical weighting </term> of <term> phrase translations </term> .
other,39-3-N03-1017,bq Our empirical results , which hold for all examined <term> language pairs </term> , suggest that the highest levels of performance can be obtained through relatively simple means : <term> heuristic learning </term> of <term> phrase translations </term> from <term> word-based alignments </term> and <term> lexical weighting </term> of <term> phrase translations </term> .
lr,34-3-N03-1018,bq We present an implementation of the <term> model </term> based on <term> finite-state models </term> , demonstrate the <term> model </term> 's ability to significantly reduce <term> character and word error rate </term> , and provide evaluation results involving <term> automatic extraction </term> of <term> translation lexicons </term> from <term> printed text </term> .
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