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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,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.
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,16-12-J05-1003,bq	language parsing ( NLP ) </term> , the <term>	approach	</term> should be applicable to many other	#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.
model,7-7-J05-1003,bq	<term> log-likelihood </term> under a <term>	baseline model	</term> ( that of <term> Collins [ 1999 ] </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.
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%.
tech,13-5-J05-1003,bq	reranking task </term> , based on the <term>	boosting approach	</term> to <term> ranking problems </term> described	#8772 We introduce a new method for the reranking task, based on theboosting approach to ranking problems described in Freund et al. (1998).
tech,9-9-J05-1003,bq	a new <term> algorithm </term> for the <term>	boosting approach	</term> which takes advantage of the <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.
tech,15-10-J05-1003,bq	obvious <term> implementation </term> of the <term>	boosting approach	</term> . We argue that the method is an	#8902 Experiments show significant efficiency gains for the new algorithm over the obvious implementation of theboosting approach.
tech,3-6-J05-1003,bq	al. ( 1998 ) </term> . We apply the <term>	boosting method	</term> to <term> parsing </term> the <term> Wall	#8789 We apply theboosting method to parsing the Wall Street Journal treebank.
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.
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.
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.
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,14-3-J05-1003,bq	<term> ranking </term> , using additional <term>	features	</term> of the <term> tree </term> as evidence	#8703 A second model then attempts to improve upon this initial ranking, using additionalfeatures of the tree as evidence.
other,19-4-J05-1003,bq	represented as an arbitrary set of <term>	features	</term> , without concerns about how these	#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,26-4-J05-1003,bq	, without concerns about how these <term>	features	</term> interact or overlap and without the	#8736 The strength of our approach is that it allows a tree to be represented as an arbitrary set of features, without concerns about how thesefeatures interact or overlap and without the need to define a derivation or a generative model which takes these features into account.
other,45-4-J05-1003,bq	generative model </term> which takes these <term>	features	</term> into account . We introduce a new	#8755 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 a derivation or a generative model which takes thesefeatures into account.
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.
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%.


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