| tech,30-12-J05-1003,bq | </term> which are naturally framed as <term> | ranking tasks | </term> , for example , <term> speech recognition | #8966 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,bq | ranking tasks </term> , for example , <term> | speech recognition | </term> , <term> machine translation </term> | #8972 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,bq | , <term> speech recognition </term> , <term> | machine translation | </term> , or <term> natural language generation | #8975 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,4-4-J05-1003,bq | </term> as evidence . The strength of our <term> | approach | </term> is that it allows a <term> tree </term> | #8714 The strength of ourapproach 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 these features into account. |
| tech,4-5-J05-1003,bq | </term> into account . We introduce a new <term> | method | </term> for the <term> reranking task </term> | #8763 We introduce a newmethod for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). |
| tech,40-4-J05-1003,bq | define a <term> derivation </term> or a <term> | generative model | </term> which takes these <term> features </term> | #8750 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. |
| tech,43-12-J05-1003,bq | <term> machine translation </term> , or <term> | natural language generation | </term> . We present a novel <term> method </term> | #8979 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,6-6-J05-1003,bq | the <term> boosting method </term> to <term> | parsing | </term> the <term> Wall Street Journal treebank | #8792 We apply the boosting method toparsing the Wall Street Journal treebank. |
| tech,6-9-J05-1003,bq | The article also introduces a new <term> | algorithm | </term> for the <term> boosting approach </term> | #8867 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,7-5-J05-1003,bq | introduce a new <term> method </term> for the <term> | reranking task | </term> , based on the <term> boosting approach | #8766 We introduce a new method for thereranking task, based on the boosting approach to ranking problems described in Freund et al. (1998). |
| tech,8-10-J05-1003,bq | significant efficiency gains for the new <term> | algorithm | </term> over the obvious <term> implementation | #8895 Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach. |
| tech,8-12-J05-1003,bq | experiments in this article are on <term> | natural language parsing ( NLP ) | </term> , the <term> approach </term> should | #8944 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. |
| tech,9-9-J05-1003,bq | a new <term> algorithm </term> for the <term> | boosting approach | </term> which takes advantage of the <term> | #8870 The article also introduces a new algorithm for theboosting approach which takes advantage of the sparsity of the feature space in the parsing data. |
