Concordance

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

measure(ment),6-8-J05-1003,bq	The new <term> model </term> achieved 89.75 % <term> F-measure </term> , a 13 % relative decrease in <term> F-measure </term> error over the <term> baseline model ’s score </term> of 88.2 % .	#8841 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%.
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,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,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.
other,10-3-J05-1003,bq	A second <term> model </term> then attempts to improve upon this initial <term> ranking </term> , using additional <term> features </term> of the <term> tree </term> as evidence .	#8699 A second model then attempts to improve upon this initialranking, using additional features of the tree as evidence.
other,12-2-J05-1003,bq	The base <term> parser </term> produces a set of <term> candidate parses </term> for each input <term> sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#8675 The base parser produces a set of candidate parses for each inputsentence, with associated probabilities that define an initial ranking of these parses.
other,19-4-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 .	#8729 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,24-2-J05-1003,bq	The base <term> parser </term> produces a set of <term> candidate parses </term> for each input <term> sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#8687 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of theseparses.
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.
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,11-1-J05-1003,bq	This article considers approaches which rerank the output of an existing <term> probabilistic parser </term> .	#8660 This article considers approaches which rerank the output of an existingprobabilistic parser.
lr-prod,8-6-J05-1003,bq	We apply the <term> boosting method </term> to <term> parsing </term> the <term> Wall Street Journal treebank </term> .	#8794 We apply the boosting method to parsing theWall Street Journal treebank.
model,34-7-J05-1003,bq	The <term> method </term> combined the <term> log-likelihood </term> under a <term> baseline model </term> ( that of <term> Collins [ 1999 ] </term> ) with evidence from an additional 500,000 <term> features </term> over <term> parse trees </term> that were not included in the original <term> model </term> .	#8833 The method combined the log-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 originalmodel.
other,20-5-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> .	#8779 We introduce a new method for the reranking task, based on the boosting approach to ranking problems described inFreund et al. ( 1998 ).
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,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.
model,7-7-J05-1003,bq	The <term> method </term> combined the <term> log-likelihood </term> under a <term> baseline model </term> ( that of <term> Collins [ 1999 ] </term> ) with evidence from an additional 500,000 <term> features </term> over <term> parse trees </term> that were not included in the original <term> model </term> .	#8806 The method combined the log-likelihood under abaseline model (that of Collins [1999]) with evidence from an additional 500,000 features over parse trees that were not included in the original model.
other,12-7-J05-1003,bq	The <term> method </term> combined the <term> log-likelihood </term> under a <term> baseline model </term> ( that of <term> Collins [ 1999 ] </term> ) with evidence from an additional 500,000 <term> features </term> over <term> parse trees </term> that were not included in the original <term> model </term> .	#8811 The method combined the log-likelihood under a baseline model (that ofCollins [ 1999 ]) with evidence from an additional 500,000 features over parse trees that were not included in the original model.


	in Help