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model,34-7-J05-1003,bq	were not included in the original <term>	model	</term> . The new <term> model </term> achieved	#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,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,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,17-3-J05-1003,bq	additional <term> features </term> of the <term>	tree	</term> as evidence . The strength of our	#8706 A second model then attempts to improve upon this initial ranking, using additional features of thetree as evidence.
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.
tech,2-3-J05-1003,bq	these <term> parses </term> . A second <term>	model	</term> then attempts to improve upon this	#8691 A secondmodel then attempts to improve upon this initial ranking, using additional features of the tree as evidence.
other,20-5-J05-1003,bq	ranking problems </term> described in <term>	Freund et al. ( 1998 )	</term> . We apply the <term> boosting method	#8779 We introduce a new method for the reranking task, based on the boosting approach to ranking problems described inFreund et al. ( 1998 ).
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,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,8-10-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,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.
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,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.
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,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.
tech,7-5-J05-1003,bq	introduce a new <term> method </term> for the <term>	reranking task	</term> , based on the <term> boosting approach	#8766 We introduce a new method for thereranking task, based on the boosting approach to ranking problems described in Freund et al. (1998).
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,21-2-J05-1003,bq	probabilities </term> that define an initial <term>	ranking	</term> of these <term> parses </term> . A second	#8684 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initialranking of these parses.
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,30-12-J05-1003,bq	</term> which are naturally framed as <term>	ranking tasks	</term> , for example , <term> speech recognition	#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.


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