Concordance

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

other,23-9-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> .	#8884 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,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.
other,12-10-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> .	#8899 Experiments show significant efficiency gains for the new algorithm over the obviousimplementation of the boosting approach.
tech,15-10-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> .	#8902 Experiments show significant efficiency gains for the new algorithm over the obvious implementation of theboosting approach.
tech,21-11-J05-1003,bq	We argue that the method is an appealing alternative — in terms of both simplicity and efficiency — to work on <term> feature selection methods </term> within <term> log-linear ( maximum-entropy ) models </term> .	#8926 We argue that the method is an appealing alternative—in terms of both simplicity and efficiency—to work onfeature selection methods within log-linear (maximum-entropy) models.
tech,25-11-J05-1003,bq	We argue that the method is an appealing alternative — in terms of both simplicity and efficiency — to work on <term> feature selection methods </term> within <term> log-linear ( maximum-entropy ) models </term> .	#8930 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.
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,16-12-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> .	#8952 Although the experiments in this article are on natural language parsing (NLP), theapproach 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,23-12-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> .	#8959 Although the experiments in this article are on natural language parsing (NLP), the approach should be applicable to many otherNLP problems which are naturally framed as ranking tasks, for example, speech recognition, machine translation, or natural language generation.
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,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.


	in Help