| other,21-2-J05-1003,ak | probabilities </term> that define an initial <term> | ranking | </term> of these <term> parses </term> . A second | #8049 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. | |
| model,2-3-J05-1003,ak | these <term> parses </term> . A second <term> | model | </term> then attempts to improve upon this | #8056 A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. | |
| other,10-3-J05-1003,ak | attempts to improve upon this initial <term> | ranking | </term> , using additional <term> features | #8064 A second model then attempts to improve upon this initial ranking , using additional features of the tree as evidence. | |
| other,17-3-J05-1003,ak | additional <term> features </term> of the <term> | tree | </term> as evidence . The strength of our | #8071 A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. | |
| other,10-4-J05-1003,ak | our approach is that it allows a <term> | tree | </term> to be represented as an arbitrary | #8085 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 these features into account. | |
| other,37-4-J05-1003,ak | and without the need to define a <term> | derivation | </term> or a <term> generative model </term> | #8112 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 these features into account. | |
| model,34-7-J05-1003,ak | were not included in the original <term> | model | </term> . The new <term> model </term> achieved | #8198 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model . | |
| model,2-8-J05-1003,ak | original <term> model </term> . The new <term> | model | </term> achieved 89.75 % <term> F-measure </term> | #8202 The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%. | |
| measure(ment),6-8-J05-1003,ak | <term> model </term> achieved 89.75 % <term> | F-measure | </term> , a 13 % relative decrease in <term> | #8206 The new model achieved 89.75% F-measure , a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%. | |
| tech,6-9-J05-1003,ak | The article also introduces a new <term> | algorithm | </term> for the <term> boosting approach </term> | #8232 The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. | |
| other,16-9-J05-1003,ak | </term> which takes advantage of the <term> | sparsity | </term> of the <term> feature space </term> | #8242 The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. | |
| tech,8-10-J05-1003,ak | significant efficiency gains for the new <term> | algorithm | </term> over the obvious implementation | #8260 Experiments show significant efficiency gains for the new algorithm over the obvious implementation of the boosting approach. |
