tech,11-1-J05-1003,bq which rerank the output of an existing <term> probabilistic parser </term> . The base <term> parser </term> produces
tech,2-2-J05-1003,bq probabilistic parser </term> . The base <term> parser </term> produces a set of <term> candidate
other,7-2-J05-1003,bq <term> parser </term> produces a set of <term> candidate parses </term> for each input <term> sentence </term>
other,12-2-J05-1003,bq candidate parses </term> for each input <term> sentence </term> , with associated <term> probabilities
other,16-2-J05-1003,bq <term> sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking
other,21-2-J05-1003,bq probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> . A second
other,24-2-J05-1003,bq initial <term> ranking </term> of these <term> parses </term> . A second <term> model </term> then
tech,2-3-J05-1003,bq these <term> parses </term> . A second <term> model </term> then attempts to improve upon this
other,10-3-J05-1003,bq attempts to improve upon this initial <term> ranking </term> , using additional <term> features </term>
other,14-3-J05-1003,bq <term> ranking </term> , using additional <term> features </term> of the <term> tree </term> as evidence
other,17-3-J05-1003,bq additional <term> features </term> of the <term> tree </term> as evidence . The strength of our
tech,4-4-J05-1003,bq </term> as evidence . The strength of our <term> approach </term> is that it allows a <term> tree </term>
other,10-4-J05-1003,bq approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary
other,19-4-J05-1003,bq represented as an arbitrary set of <term> features </term> , without concerns about how these
other,26-4-J05-1003,bq , without concerns about how these <term> features </term> interact or overlap and without the
other,37-4-J05-1003,bq overlap and without the need to define a <term> derivation </term> or a <term> generative model </term>
tech,40-4-J05-1003,bq define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term>
other,45-4-J05-1003,bq generative model </term> which takes these <term> features </term> into account . We introduce a new
tech,4-5-J05-1003,bq </term> into account . We introduce a new <term> method </term> for the <term> reranking task </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
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