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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,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,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,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,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.
other,25-7-J05-1003,bq	additional 500,000 <term> features </term> over <term>	parse trees	</term> that were not included in the original	#8824 The method combined the log-likelihood under a baseline model (that of Collins [1999]) with evidence from an additional 500,000 features overparse trees that were not included in the original model.
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.
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,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,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.
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,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.
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%.
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.
tech,36-12-J05-1003,bq	ranking tasks </term> , for example , <term>	speech recognition	</term> , <term> machine translation </term>	#8972 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,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,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,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.
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.


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