| 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,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,2-3-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. | |
| 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,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,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),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,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,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. | |
| 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. | |
| 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,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,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. | |
| 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,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,45-4-J05-1003,ak | generative model </term> which takes these <term> | features | </term> into account . We introduce a new | #8120 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 a generative model which takes thesefeatures into account. | |
| 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,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,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,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. |
