Concordance

Query J05-1003 44 >
Negative filter probabilistic, parser 43 (1,376.7 per million)

other,4-7-J05-1003,ak	The method combined the <term> log-likelihood under a baseline model </term> ( that of Collins [ 1999 ] ) 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> .	#8168 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.
other,23-7-J05-1003,ak	The method combined the <term> log-likelihood under a baseline model </term> ( that of Collins [ 1999 ] ) 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> .	#8187 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,ak	The method combined the <term> log-likelihood under a baseline model </term> ( that of Collins [ 1999 ] ) 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> .	#8189 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,ak	The method combined the <term> log-likelihood under a baseline model </term> ( that of Collins [ 1999 ] ) 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> .	#8198 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.
model,2-8-J05-1003,ak	The new <term> model </term> achieved 89.75 % <term> F-measure </term> , a 13 % relative decrease in <term> F-measure error </term> over the <term> baseline model ’s </term> score of 88.2 % .	#8202 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,ak	The new <term> model </term> achieved 89.75 % <term> F-measure </term> , a 13 % relative decrease in <term> F-measure error </term> over the <term> baseline model ’s </term> score of 88.2 % .	#8206 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,ak	The new <term> model </term> achieved 89.75 % <term> F-measure </term> , a 13 % relative decrease in <term> F-measure error </term> over the <term> baseline model ’s </term> score of 88.2 % .	#8214 The new model achieved 89.75% F-measure, a 13% relative decrease inF-measure error over the baseline model’s score of 88.2%.
model,18-8-J05-1003,ak	The new <term> model </term> achieved 89.75 % <term> F-measure </term> , a 13 % relative decrease in <term> F-measure error </term> over the <term> baseline model ’s </term> score of 88.2 % .	#8218 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,ak	The article also introduces a new <term> algorithm </term> for the <term> boosting approach </term> which takes advantage of the <term> sparsity </term> of the <term> feature space </term> in the <term> parsing data </term> .	#8232 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,ak	The article also introduces a new <term> algorithm </term> for the <term> boosting approach </term> which takes advantage of the <term> sparsity </term> of the <term> feature space </term> in the <term> parsing data </term> .	#8235 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,ak	The article also introduces a new <term> algorithm </term> for the <term> boosting approach </term> which takes advantage of the <term> sparsity </term> of the <term> feature space </term> in the <term> parsing data </term> .	#8242 The article also introduces a new algorithm for the boosting approach which takes advantage of thesparsity of the feature space in the parsing data.
other,19-9-J05-1003,ak	The article also introduces a new <term> algorithm </term> for the <term> boosting approach </term> which takes advantage of the <term> sparsity </term> of the <term> feature space </term> in the <term> parsing data </term> .	#8245 The article also introduces a new algorithm for the boosting approach which takes advantage of the sparsity of thefeature space in the parsing data.
other,23-9-J05-1003,ak	The article also introduces a new <term> algorithm </term> for the <term> boosting approach </term> which takes advantage of the <term> sparsity </term> of the <term> feature space </term> in the <term> parsing data </term> .	#8249 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,ak	Experiments show significant efficiency gains for the new <term> algorithm </term> over the obvious implementation of the <term> boosting approach </term> .	#8260 Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach.
tech,15-10-J05-1003,ak	Experiments show significant efficiency gains for the new <term> algorithm </term> over the obvious implementation of the <term> boosting approach </term> .	#8267 Experiments show significant efficiency gains for the new algorithm over the obvious implementation of theboosting approach.
tech,21-11-J05-1003,ak	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> .	#8291 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.
model,25-11-J05-1003,ak	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> .	#8295 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,ak	Although the experiments in this article are on <term> natural language parsing ( NLP ) </term> , the approach 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> .	#8309 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,23-12-J05-1003,ak	Although the experiments in this article are on <term> natural language parsing ( NLP ) </term> , the approach 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> .	#8324 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.
other,30-12-J05-1003,ak	Although the experiments in this article are on <term> natural language parsing ( NLP ) </term> , the approach 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> .	#8331 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.


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