| 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%. | |
| 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). | |
| 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,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. | |
| 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. | |
| 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. | |
| other,10-3-J05-1003,ak | attempts to improve upon this initial <term> | ranking | </term> , using additional <term> features </term> | #8064 A second model then attempts to improve upon this initialranking, using additional features of the tree as evidence. | |
| 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,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,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. | |
| 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,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,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. | |
| 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. | |
| 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,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,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. | |
| 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,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,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. |
