| 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,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,10-4-J05-1003,ak | of 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 atree 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,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. | |
| 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 originalmodel. | |
| 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,7-5-J05-1003,ak | We introduce a new method for the <term> | reranking task | </term> , based on the <term> boosting approach | #8131 We introduce a new method for thereranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). | |
| 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. | |
| 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%. | |
| 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. | |
| 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. | |
| 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. | |
| 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,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. | |
| 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,2-8-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%. | |
| 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). | |
| 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. | |
| tech,36-12-J05-1003,ak | ranking tasks </term> , for example , <term> | speech recognition | </term> , <term> machine translation </term> | #8337 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. |
