tech,10-1-H01-1041,bq developing a <term> Korean-to-English machine translation system </term><term> CCLINC ( Common Coalition
tool,1-2-H01-1041,bq </term> . The <term> CCLINC Korean-to-English translation system </term> consists of two <term> core
tech,5-4-H01-1041,bq arguments </term> ) . ( ii ) High quality <term> translation </term> via <term> word sense disambiguation
other,18-6-H01-1041,bq the <term> system </term> produces the <term> translation output </term> sufficient for content understanding
tech,30-1-H01-1042,bq to the <term> output </term> of <term> machine translation ( MT ) systems </term> . We believe that
other,18-2-H01-1042,bq language learning process </term> , the <term> translation process </term> and the <term> development </term>
tech,24-2-H01-1042,bq the <term> development </term> of <term> machine translation systems </term> . This , the first experiment
other,16-6-H01-1042,bq duplicating the experiment using <term> machine translation output </term> . Subjects were given a set
other,5-8-H01-1042,bq Some of the extracts were <term> expert human translations </term> , others were <term> machine translation
other,11-8-H01-1042,bq translations </term> , others were <term> machine translation outputs </term> . The subjects were given
other,19-9-H01-1042,bq sample output to be an <term> expert human translation </term> or a <term> machine translation </term>
other,24-9-H01-1042,bq human translation </term> or a <term> machine translation </term> . Additionally , they were asked
tech,23-1-P01-1004,bq <term> retrieval performance </term> of a <term> translation memory system </term> . We take a selection
tech,4-3-P01-1007,bq complexity </term> . For example , after <term> translation </term> into an equivalent <term> RCG </term>
lr,12-2-P01-1008,bq </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
model,21-1-N03-1017,bq , previously proposed <term> phrase-based translation models </term> . Within our framework , we
other,30-3-N03-1017,bq <term> heuristic learning </term> of <term> phrase translations </term> from <term> word-based alignments </term>
other,39-3-N03-1017,bq <term> lexical weighting </term> of <term> phrase translations </term> . Surprisingly , learning <term> phrases
lr,34-3-N03-1018,bq <term> automatic extraction </term> of <term> translation lexicons </term> from <term> printed text </term>
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