| 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 newalgorithm 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 of | #8260 Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach. | |
| tech,1-2-J05-1003,ak | <term> probabilistic parser </term> . The <term> | base parser | </term> produces a set of <term> candidate | #8029 Thebase parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. | |
| model,18-8-J05-1003,ak | <term> F-measure error </term> over the <term> | baseline model ’s | </term> score of 88.2 % . The article also | #8218 The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over thebaseline model ’s score of 88.2%. | |
| tech,9-9-J05-1003,ak | a new <term> algorithm </term> for the <term> | boosting approach | </term> which takes advantage of the <term> | #8235 The article also introduces a new algorithm for theboosting approach which takes advantage of the sparsity of the feature space in the parsing data. | |
| tech,15-10-J05-1003,ak | the obvious implementation of the <term> | boosting approach | </term> . We argue that the method is an | #8267 Experiments show significant efficiency gains for the new algorithm over the obvious implementation of theboosting approach. | |
| tech,13-5-J05-1003,ak | reranking task </term> , based on the <term> | boosting approach to ranking problems | </term> described in Freund et al. ( 1998 | #8137 We introduce a new method for the reranking task, based on theboosting approach to ranking problems described in Freund et al. (1998). | |
| tech,3-6-J05-1003,ak | Freund et al. ( 1998 ) . We apply the <term> | boosting method | </term> to parsing the <term> Wall Street Journal | #8154 We apply theboosting method to parsing the Wall Street Journal treebank. | |
| other,7-2-J05-1003,ak | base parser </term> produces a set of <term> | candidate parses | </term> for each <term> input sentence </term> | #8035 The base parser produces a set ofcandidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses. | |
| tech,21-11-J05-1003,ak | simplicity and efficiency — to work on <term> | feature selection methods | </term> within <term> log-linear ( maximum-entropy | #8291 We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work onfeature selection methods within log-linear (maximum-entropy) models. | |
| other,19-9-J05-1003,ak | of the <term> sparsity </term> of the <term> | feature space | </term> in the <term> parsing data </term> . | #8245 The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of thefeature space in the parsing data. | |
| other,14-3-J05-1003,ak | <term> ranking </term> , using additional <term> | features | </term> of the <term> tree </term> as evidence | #8068 A second model then attempts to improve upon this initial ranking, using additionalfeatures of the tree as evidence. | |
| other,19-4-J05-1003,ak | represented as an arbitrary set of <term> | features | </term> , without concerns about how these | #8094 The strength of our approach is that it allows a tree to be represented as an arbitrary set offeatures, 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,26-4-J05-1003,ak | , without concerns about how these <term> | features | </term> interact or overlap and without the | #8101 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,45-4-J05-1003,ak | generative model </term> which takes these <term> | features | </term> into account . We introduce a new | #8120 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,23-7-J05-1003,ak | evidence from an additional 500,000 <term> | features | </term> over <term> parse trees </term> that | #8187 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000features over parse trees that were not included in the original model. | |
| 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%. | |
| measure(ment),14-8-J05-1003,ak | </term> , a 13 % relative decrease in <term> | F-measure error | </term> over the <term> baseline model ’s </term> | #8214 The new model achieved 89.75% F-measure, a 13% relative decrease inF-measure error over the baseline model’s score of 88.2%. | |
| model,40-4-J05-1003,ak | define a <term> derivation </term> or a <term> | generative model | </term> which takes these <term> features </term> | #8115 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 agenerative model which takes these features into account. | |
| other,11-2-J05-1003,ak | <term> candidate parses </term> for each <term> | input sentence | </term> , with associated <term> probabilities | #8039 The base parser produces a set of candidate parses for eachinput sentence, with associated probabilities that define an initial ranking of these parses. |
