Concordance

Query J05-1003 53 >
Multilevel Sort 53 (2,471.0 per million)

tech,30-12-J05-1003,bq	Although the experiments in this article are on <term> natural language parsing ( NLP ) </term> , the <term> approach </term> should be applicable to many other <term> NLP problems </term> which are naturally framed as <term> ranking tasks </term> , for example , <term> speech recognition </term> , <term> machine translation </term> , or <term> natural language generation </term> .	#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	Although the experiments in this article are on <term> natural language parsing ( NLP ) </term> , the <term> approach </term> should be applicable to many other <term> NLP problems </term> which are naturally framed as <term> ranking tasks </term> , for example , <term> speech recognition </term> , <term> machine translation </term> , or <term> natural language generation </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	Although the experiments in this article are on <term> natural language parsing ( NLP ) </term> , the <term> approach </term> should be applicable to many other <term> NLP problems </term> which are naturally framed as <term> ranking tasks </term> , for example , <term> speech recognition </term> , <term> machine translation </term> , or <term> natural language generation </term> .	#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	The strength of our <term> approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary set of <term> features </term> , without concerns about how these <term> features </term> interact or overlap and without the need to define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term> into account .	#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	We introduce a new <term> method </term> for the <term> reranking task </term> , based on the <term> boosting approach </term> to <term> ranking problems </term> described in <term> Freund et al. ( 1998 ) </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	The strength of our <term> approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary set of <term> features </term> , without concerns about how these <term> features </term> interact or overlap and without the need to define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term> into account .	#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	Although the experiments in this article are on <term> natural language parsing ( NLP ) </term> , the <term> approach </term> should be applicable to many other <term> NLP problems </term> which are naturally framed as <term> ranking tasks </term> , for example , <term> speech recognition </term> , <term> machine translation </term> , or <term> natural language generation </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	We apply the <term> boosting method </term> to <term> parsing </term> the <term> Wall Street Journal treebank </term> .	#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> which takes advantage of the <term> sparsity of the feature space </term> in the <term> parsing data </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	We introduce a new <term> method </term> for the <term> reranking task </term> , based on the <term> boosting approach </term> to <term> ranking problems </term> described in <term> Freund et al. ( 1998 ) </term> .	#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	Experiments show significant efficiency gains for the new <term> algorithm </term> over the obvious <term> implementation </term> of the <term> boosting approach </term> .	#8895 Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach.
tech,8-12-J05-1003,bq	Although the experiments in this article are on <term> natural language parsing ( NLP ) </term> , the <term> approach </term> should be applicable to many other <term> NLP problems </term> which are naturally framed as <term> ranking tasks </term> , for example , <term> speech recognition </term> , <term> machine translation </term> , or <term> natural language generation </term> .	#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	The article also introduces a new <term> algorithm </term> for the <term> boosting approach </term> which takes advantage of the <term> sparsity of the feature space </term> in the <term> parsing data </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.


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