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tech,39-12-J05-1003,bq	, <term> speech recognition </term> , <term>	machine translation	</term> , or <term> natural language generation	#8975 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 as ranking tasks, for example, speech recognition,machine translation, or natural language generation.
tech,40-4-J05-1003,bq	define a <term> derivation </term> or a <term>	generative model	</term> which takes these <term> features </term>	#8750 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 agenerative 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.
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 ).
measure(ment),14-8-J05-1003,bq	</term> , a 13 % relative decrease in <term>	F-measure	</term> error over the <term> baseline model	#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%.
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,43-12-J05-1003,bq	<term> machine translation </term> , or <term>	natural language generation	</term> . We present a novel <term> method </term>	#8979 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 as ranking tasks, for example, speech recognition, machine translation, ornatural language generation.
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,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,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.
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.
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.
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,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.
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).
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,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.
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,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.


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