measure(ment),6-8-J05-1003,bq <term> model </term> achieved 89.75 % <term> F-measure </term> , a 13 % relative decrease in <term>
tech,7-5-J05-1003,bq introduce a new <term> method </term> for the <term> reranking task </term> , based on the <term> boosting approach
tech,30-12-J05-1003,bq </term> which are naturally framed as <term> ranking tasks </term> , for example , <term> speech recognition
tech,36-12-J05-1003,bq ranking tasks </term> , for example , <term> speech recognition </term> , <term> machine translation </term>
tech,39-12-J05-1003,bq , <term> speech recognition </term> , <term> machine translation </term> , or <term> natural language generation
tech,8-12-J05-1003,bq experiments in this article are on <term> natural language parsing ( NLP ) </term> , the <term> approach </term> should
other,10-3-J05-1003,bq attempts to improve upon this initial <term> ranking </term> , using additional <term> features </term>
other,12-2-J05-1003,bq candidate parses </term> for each input <term> sentence </term> , with associated <term> probabilities
other,19-4-J05-1003,bq represented as an arbitrary set of <term> features </term> , without concerns about how these
other,24-2-J05-1003,bq initial <term> ranking </term> of these <term> parses </term> . A second <term> model </term> then
tech,25-11-J05-1003,bq feature selection methods </term> within <term> log-linear ( maximum-entropy ) models </term> . Although the experiments in this
other,23-9-J05-1003,bq of the feature space </term> in the <term> parsing data </term> . Experiments show significant efficiency
tech,11-1-J05-1003,bq which rerank the output of an existing <term> probabilistic parser </term> . The base <term> parser </term> produces
lr-prod,8-6-J05-1003,bq method </term> to <term> parsing </term> the <term> Wall Street Journal treebank </term> . The <term> method </term> combined
model,34-7-J05-1003,bq were not included in the original <term> model </term> . The new <term> model </term> achieved
other,20-5-J05-1003,bq ranking problems </term> described in <term> Freund et al. ( 1998 ) </term> . We apply the <term> boosting method
tech,15-10-J05-1003,bq obvious <term> implementation </term> of the <term> boosting approach </term> . We argue that the method is an
tech,43-12-J05-1003,bq <term> machine translation </term> , or <term> natural language generation </term> . We present a novel <term> method </term>
model,7-7-J05-1003,bq <term> log-likelihood </term> under a <term> baseline model </term> ( that of <term> Collins [ 1999 ] </term>
other,12-7-J05-1003,bq <term> baseline model </term> ( that of <term> Collins [ 1999 ] </term> ) with evidence from an additional
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