| 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. | |
| other,37-4-J05-1003,ak | overlap 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 aderivation or a generative model which takes these features into account. | |
| 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. | |
| 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. | |
| other,4-7-J05-1003,ak | treebank </term> . The method combined the <term> | log-likelihood under a baseline model | </term> ( that of Collins [ 1999 ] ) with | #8168 The method combined thelog-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,25-11-J05-1003,ak | feature selection methods </term> within <term> | log-linear ( maximum-entropy ) models | </term> . Although the experiments in this | #8295 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. | |
| tech,39-12-J05-1003,ak | , <term> speech recognition </term> , <term> | machine translation | </term> , or <term> natural language generation | #8340 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. | |
| model,2-3-J05-1003,ak | these <term> parses </term> . A second <term> | model | </term> then attempts to improve upon this | #8056 A secondmodel then attempts to improve upon this initial ranking, using additional features of the tree as evidence. |
