| 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. | |
| 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,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,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. | |
| 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. | |
| 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,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,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. | |
| 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. | |
| 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. | |
| 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),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,21-2-J05-1003,ak | probabilities </term> that define an initial <term> | ranking | </term> of these <term> parses </term> . A second | #8049 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initialranking of these parses. | |
| 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. | |
| 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. | |
| 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,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,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,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. |
