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,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>
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
lr,34-3-N03-1018,bq <term> automatic extraction </term> of <term> translation lexicons </term> from <term> printed text </term>
other,5-1-N03-2006,bq N-grams </term> . In order to boost the <term> translation quality </term> of <term> EBMT </term> based
tech,11-1-N03-2036,bq model </term> for <term> statistical machine translation </term> that uses a much simpler set of <term>
other,1-2-N03-2036,bq phrase-based models </term> . The <term> units of translation </term> are <term> blocks </term> - pairs of <term>
tech,6-2-P03-1050,bq </term> is based on <term> statistical machine translation </term> and it uses an <term> English stemmer
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