Concordance

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

tech,13-5-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> .	#8772 We introduce a new method for the reranking task, based on theboosting approach to ranking problems described in Freund et al. (1998).
other,16-5-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> .	#8775 We introduce a new method for the reranking task, based on the boosting approach toranking problems described in Freund et al. (1998).
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,3-6-J05-1003,bq	We apply the <term> boosting method </term> to <term> parsing </term> the <term> Wall Street Journal treebank </term> .	#8789 We apply theboosting method to parsing the Wall Street Journal treebank.
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.
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.
tech,1-7-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> .	#8800 Themethod 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 original model.
other,4-7-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> .	#8803 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,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.
other,23-7-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> .	#8822 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.
other,25-7-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> .	#8824 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features overparse trees that were not included in the original model.
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.
tech,2-8-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 % .	#8837 The newmodel achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%.
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%.
measure(ment),14-8-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 % .	#8849 The new model achieved 89.75% F-measure, a 13% relative decrease inF-measure error over the baseline model’s score of 88.2%.
measure(ment),18-8-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 % .	#8853 The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over thebaseline model ’s score of 88.2%.
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,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.
other,16-9-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> .	#8877 The article also introduces a new algorithm for the boosting approach which takes advantage of thesparsity of the feature space in the parsing data.


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