tech,4-2-H01-1070,bq paper also proposes <term> rule-reduction algorithm </term> applying <term> mutual information </term>
tech,1-3-H01-1070,bq error-correction rules </term> . Our <term> algorithm </term> reported more than 99 % <term> accuracy
tech,3-2-P01-1008,bq We present an <term> unsupervised learning algorithm </term> for <term> identification of paraphrases
tech,31-2-P01-1047,bq sensitive logic </term> , and a <term> learning algorithm </term> from <term> structured data </term> (
tech,3-3-N03-1004,bq present our <term> multi-level answer resolution algorithm </term> that combines results from the <term>
tech,6-4-N03-1004,bq effectiveness of our <term> answer resolution algorithm </term> show a 35.0 % relative improvement
tech,8-1-N03-1017,bq translation model </term> and <term> decoding algorithm </term> that enables us to evaluate and compare
tech,10-3-N03-2017,bq <term> constraint </term> in two different <term> algorithms </term> . The results show that it can provide
tech,17-2-P03-1051,bq uses it to bootstrap an <term> unsupervised algorithm </term> to build the <term> Arabic word segmenter
tech,1-3-P03-1051,bq unsegmented Arabic corpus </term> . The <term> algorithm </term> uses a <term> trigram language model
tech,9-5-P03-1051,bq accuracy </term> , we use an <term> unsupervised algorithm </term> for automatically acquiring new <term>
tech,9-7-P03-1051,bq state-of-the-art performance and the <term> algorithm </term> can be used for many <term> highly
tech,26-3-C04-1035,bq run two different <term> machine learning algorithms </term> : <term> SLIPPER </term> , a <term> rule-based
tech,33-3-C04-1035,bq SLIPPER </term> , a <term> rule-based learning algorithm </term> , and <term> TiMBL </term> , a <term> memory-based
tech,17-3-C04-1080,bq </term> that can be achieved by the <term> algorithms </term> , we present a method of <term> HMM
tech,18-5-C04-1096,bq situations , and built a <term> generation algorithm </term> based on the results . The evaluation
training material </term> available to the algorithm . Testing the <term> lemma-based model </term>
</term> . The framework is composed of a novel algorithm to efficiently compute the <term> co-occurrence
tech,14-5-P04-2005,bq a <term> second-order vector co-occurrence algorithm </term> on standard <term> WSD datasets </term>
tech,4-1-H05-1012,bq presents a <term> maximum entropy word alignment algorithm </term> for <term> Arabic-English </term> based
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