lr,8-6-N01-1003,bq rules </term> automatically learned from <term> training data </term> . We show that the trained <term>
other,18-3-P01-1070,bq including the influence of various <term> training and testing factors </term> on <term> predictive
lr,15-1-N03-1001,bq <term> manual transcription </term> of <term> training data </term> . The method combines <term> domain
tech,4-3-N03-1001,bq </term> . In our method , <term> unsupervised training </term> is first used to train a <term> phone
lr,2-1-N03-2003,bq <term> tagging </term> result . Sources of <term> training data </term> suitable for <term> language modeling
lr,7-2-N03-2003,bq limited . In this paper , we show how <term> training data </term> can be supplemented with <term>
bootstrapping procedure </term> is implemented as training two <term> successive learners </term> . First
tech,1-4-N03-2036,bq trigram language model </term> . During <term> training </term> , the <term> blocks </term> are learned
lr,27-2-P03-1050,bq <term> parallel corpus </term> as its sole <term> training resources </term> . No <term> parallel text
other,7-3-P03-1050,bq parallel text </term> is needed after the <term> training phase </term> . <term> Monolingual , unannotated
lr,34-5-P03-1051,bq expanded <term> vocabulary </term> and <term> training corpus </term> . The resulting <term> Arabic
lr,11-2-P03-1058,bq automatically acquire <term> sense-tagged training data </term> from <term> English-Chinese parallel
tech,24-3-C04-1080,bq </term> , we present a method of <term> HMM training </term> that improves <term> accuracy </term>
</term> that improves <term> accuracy </term> when training of <term> lexical probabilities </term> is
lr,24-3-C04-1112,bq classifier </term> , therefore augmenting the <term> training material </term> available to the algorithm
lr,13-1-H05-1012,bq Arabic-English </term> based on <term> supervised training data </term> . We demonstrate that it is
lr,8-2-H05-1012,bq demonstrate that it is feasible to create <term> training material </term> for problems in <term> machine
tech,16-3-H05-1095,bq <term> phrases </term> , as well as a <term> training method </term> based on the maximization
lr,17-5-H05-1095,bq allows to better generalize from the <term> training data </term> . This paper investigates some
tech,8-1-P05-1069,bq In this paper , we present a novel <term> training method </term> for a <term> localized phrase-based
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