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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.
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,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,16-2-J05-1003,bq	<term> sentence </term> , with associated <term>	probabilities	</term> that define an initial <term> ranking	#8679 The base parser produces a set of candidate parses for each input sentence, with associatedprobabilities that define an initial ranking of these parses.
other,16-9-J05-1003,bq	</term> which takes advantage of the <term>	sparsity of the feature space	</term> in the <term> parsing data </term> .	#8877 The article also introduces a new algorithm for the boosting approach which takes advantage of thesparsity of the feature space in the parsing data.
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.
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,10-3-J05-1003,bq	attempts to improve upon this initial <term>	ranking	</term> , using additional <term> features </term>	#8699 A second model then attempts to improve upon this initialranking, using additional features of the tree as evidence.
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.
tech,4-5-J05-1003,bq	</term> into account . We introduce a new <term>	method	</term> for the <term> reranking task </term>	#8763 We introduce a newmethod for the reranking task, based on the boosting approach to ranking problems described in Freund et al. (1998).
tech,11-1-J05-1003,bq	which rerank the output of an existing <term>	probabilistic parser	</term> . The base <term> parser </term> produces	#8660 This article considers approaches which rerank the output of an existingprobabilistic parser.
tech,25-11-J05-1003,bq	feature selection methods </term> within <term>	log-linear ( maximum-entropy ) models	</term> . Although the experiments in this	#8930 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.
other,16-5-J05-1003,bq	the <term> boosting approach </term> to <term>	ranking problems	</term> described in <term> Freund et al. (	#8775 We introduce a new method for the reranking task, based on the boosting approach toranking problems described in Freund et al. (1998).
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.
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,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.
tech,1-7-J05-1003,bq	Street Journal treebank </term> . The <term>	method	</term> combined the <term> log-likelihood </term>	#8800 Themethod 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 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.
other,12-2-J05-1003,bq	candidate parses </term> for each input <term>	sentence	</term> , with associated <term> probabilities	#8675 The base parser produces a set of candidate parses for each inputsentence, with associated probabilities that define an initial ranking of these parses.
other,24-2-J05-1003,bq	initial <term> ranking </term> of these <term>	parses	</term> . A second <term> model </term> then	#8687 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of theseparses.


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