| other,23-9-J05-1003,bq | of the feature space </term> in the <term> | parsing data | </term> . Experiments show significant efficiency | #8884 The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in theparsing data. |
| 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. |
| 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. |
| tech,15-10-J05-1003,bq | obvious <term> implementation </term> of the <term> | boosting approach | </term> . We argue that the method is an | #8902 Experiments show significant efficiency gains for the new algorithm over the obvious implementation of theboosting approach. |
| tech,13-5-J05-1003,bq | reranking task </term> , based on the <term> | boosting approach | </term> to <term> ranking problems </term> described | #8772 We introduce a new method for the reranking task, based on theboosting approach to ranking problems described in Freund et al. (1998). |
| measure(ment),18-8-J05-1003,bq | <term> F-measure </term> error over the <term> | baseline model ’s score | </term> of 88.2 % . The article also introduces | #8853 The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over thebaseline model ’s score of 88.2%. |
| 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. |
| other,26-4-J05-1003,bq | , without concerns about how these <term> | features | </term> interact or overlap and without the | #8736 The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how thesefeatures interact or overlap and without the need to define a derivation or a generative model which takes these features into account. |
| other,24-2-J05-1003,bq | initial <term> ranking </term> of these <term> | parses | </term> . A second <term> model </term> then | #8687 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of theseparses. |
| other,45-4-J05-1003,bq | generative model </term> which takes these <term> | features | </term> into account . We introduce a new | #8755 The strength of our approach is that it allows a tree 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 thesefeatures into account. |
| 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,6-6-J05-1003,bq | the <term> boosting method </term> to <term> | parsing | </term> the <term> Wall Street Journal treebank | #8792 We apply the boosting method toparsing the Wall Street Journal treebank. |
| tech,25-11-J05-1003,bq | feature selection methods </term> within <term> | log-linear ( maximum-entropy ) models | </term> . Although the experiments in this | #8930 We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work on feature selection methods withinlog-linear ( maximum-entropy ) models. |
