| other,24-2-J05-1003,ak | initial <term> ranking </term> of these <term> | parses | </term> . A second <term> model </term> then | #8052 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of theseparses. | |
| other,23-9-J05-1003,ak | the <term> feature space </term> in the <term> | parsing data | </term> . Experiments show significant efficiency | #8249 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,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. | |
| 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). | |
| 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%. | |
| other,30-12-J05-1003,ak | </term> which are naturally framed as <term> | ranking tasks | </term> , for example , <term> speech recognition | #8331 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. | |
| lr,8-6-J05-1003,ak | boosting method </term> to parsing the <term> | Wall Street Journal treebank | </term> . The method combined the <term> log-likelihood | #8159 We apply the boosting method to parsing theWall Street Journal treebank. | |
| tech,43-12-J05-1003,ak | <term> machine translation </term> , or <term> | natural language generation | </term> . We present a novel method for discovering | #8344 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, ornatural language generation. | |
| 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,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. | |
| tech,8-12-J05-1003,ak | experiments in this article are on <term> | natural language parsing ( NLP ) | </term> , the approach should be applicable | #8309 Although the experiments in this article are onnatural 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,2-8-J05-1003,ak | original <term> model </term> . The new <term> | model | </term> achieved 89.75 % <term> F-measure </term> | #8202 The newmodel achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%. | |
| 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. | |
| other,16-2-J05-1003,ak | input sentence </term> , with associated <term> | probabilities | </term> that define an initial <term> ranking | #8044 The base parser produces a set of candidate parses for each input sentence, with associatedprobabilities that define an initial ranking of these parses. | |
| tech,11-1-J05-1003,ak | which rerank the output of an existing <term> | probabilistic parser | </term> . The <term> base parser </term> produces | #8025 This article considers approaches which rerank the output of an existingprobabilistic parser. | |
| 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 newalgorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. | |
| 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. | |
| 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 thetree as evidence. |
