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tech,2-2-J05-1003,bq	probabilistic parser </term> . The base <term>	parser	</term> produces a set of <term> candidate	#8665 The baseparser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses.
tech,8-10-J05-1003,bq	significant efficiency gains for the new <term>	algorithm	</term> over the obvious <term> implementation	#8895 Experiments show significant efficiency gains for the newalgorithm over the obvious implementation of the boosting approach.
measure(ment),18-8-J05-1003,bq	<term> F-measure </term> error over the <term>	baseline model ’s score	</term> of 88.2 % . The article also introduces	#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%.
measure(ment),6-8-J05-1003,bq	<term> model </term> achieved 89.75 % <term>	F-measure	</term> , a 13 % relative decrease in <term>	#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%.
other,23-12-J05-1003,bq	should be applicable to many other <term>	NLP problems	</term> which are naturally framed as <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.
other,23-7-J05-1003,bq	evidence from an additional 500,000 <term>	features	</term> over <term> parse trees </term> that	#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.
tech,6-9-J05-1003,bq	The article also introduces a new <term>	algorithm	</term> for the <term> boosting approach </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,2-8-J05-1003,bq	original <term> model </term> . The new <term>	model	</term> achieved 89.75 % <term> F-measure </term>	#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%.
other,12-7-J05-1003,bq	<term> baseline model </term> ( that of <term>	Collins [ 1999 ]	</term> ) with evidence from an additional	#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.
tech,4-4-J05-1003,bq	</term> as evidence . The strength of our <term>	approach	</term> is that it allows a <term> tree </term>	#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,21-11-J05-1003,bq	simplicity and efficiency — to work on <term>	feature selection methods	</term> within <term> log-linear ( maximum-entropy	#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.
other,37-4-J05-1003,bq	overlap and without the need to define a <term>	derivation	</term> or a <term> generative model </term>	#8747 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 aderivation or a generative model which takes these features into account.
other,23-9-J05-1003,bq	of the feature space </term> in the <term>	parsing data	</term> . Experiments show significant efficiency	#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,6-6-J05-1003,bq	the <term> boosting method </term> to <term>	parsing	</term> the <term> Wall Street Journal treebank	#8792 We apply the boosting method toparsing the Wall Street Journal treebank.
other,12-10-J05-1003,bq	<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.
lr-prod,8-6-J05-1003,bq	method </term> to <term> parsing </term> the <term>	Wall Street Journal treebank	</term> . The <term> method </term> combined	#8794 We apply the boosting method to parsing theWall Street Journal treebank.
other,10-4-J05-1003,bq	approach </term> is that it allows a <term>	tree	</term> to be represented as an arbitrary	#8720 The strength of our approach is that it allows atree 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.
other,4-7-J05-1003,bq	The <term> method </term> combined the <term>	log-likelihood	</term> under a <term> baseline 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.
tech,8-12-J05-1003,bq	experiments in this article are on <term>	natural language parsing ( NLP )	</term> , the <term> approach </term> should	#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,7-2-J05-1003,bq	<term> parser </term> produces a set of <term>	candidate parses	</term> for each input <term> sentence </term>	#8670 The base parser produces a set ofcandidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses.


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