Concordance

Query J05-1003 53 >
Shuffle 53 (2,471.0 per million)

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,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.
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).
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,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,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,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.
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,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.
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.
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.
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.
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.


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