| tech,11-1-J05-1003,bq | which rerank the output of an existing <term> | probabilistic parser | </term> . The base <term> parser </term> produces | #8660 This article considers approaches which rerank the output of an existingprobabilistic parser. |
| other,16-2-J05-1003,bq | <term> sentence </term> , with associated <term> | probabilities | </term> that define an initial <term> ranking | #8679 The base parser produces a set of candidate parses for each input sentence, with associatedprobabilities that define an initial ranking of these parses. |
| other,21-2-J05-1003,bq | probabilities </term> that define an initial <term> | ranking | </term> of these <term> parses </term> . A second | #8684 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initialranking of these parses. |
| other,10-3-J05-1003,bq | attempts to improve upon this initial <term> | ranking | </term> , using additional <term> features </term> | #8699 A second model then attempts to improve upon this initialranking, using additional features of the tree as evidence. |
| other,16-5-J05-1003,bq | the <term> boosting approach </term> to <term> | ranking problems | </term> described in <term> Freund et al. ( | #8775 We introduce a new method for the reranking task, based on the boosting approach toranking problems described in Freund et al. (1998). |
| tech,30-12-J05-1003,bq | </term> which are naturally framed as <term> | ranking tasks | </term> , for example , <term> speech recognition | #8966 Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed asranking tasks, for example, speech recognition, machine translation, or natural language generation. |
| tech,7-5-J05-1003,bq | introduce a new <term> method </term> for the <term> | reranking task | </term> , based on the <term> boosting approach | #8766 We introduce a new method for thereranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). |
| other,12-2-J05-1003,bq | candidate parses </term> for each input <term> | sentence | </term> , with associated <term> probabilities | #8675 The base parser produces a set of candidate parses for each inputsentence, with associated probabilities that define an initial ranking of these parses. |
| other,16-9-J05-1003,bq | </term> which takes advantage of the <term> | sparsity of the feature space | </term> in the <term> parsing data </term> . | #8877 The article also introduces a new algorithm for the boosting approach which takes advantage of thesparsity of the feature space in the parsing data. |
| tech,36-12-J05-1003,bq | ranking tasks </term> , for example , <term> | speech recognition | </term> , <term> machine translation </term> | #8972 Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many other NLP problems which are naturally framed as ranking tasks, for example,speech recognition, machine translation, or natural language generation. |
| other,17-3-J05-1003,bq | additional <term> features </term> of the <term> | tree | </term> as evidence . The strength of our | #8706 A second model then attempts to improve upon this initial ranking, using additional features of thetree as evidence. |
| other,10-4-J05-1003,bq | approach </term> is that it allows a <term> | tree | </term> to be represented as an arbitrary | #8720 The strength of our approach is that it allows atree to be represented as an arbitrary set of features, without concerns about how these features interact or overlap and without the need to define a derivation or a generative model which takes these features into account. |
| 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 | #8794 We apply the boosting method to parsing theWall Street Journal treebank. |
