Concordance

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

tech,11-1-J05-1003,bq	This article considers approaches which rerank the output of an existing <term> probabilistic parser </term> .	#8660 This article considers approaches which rerank the output of an existingprobabilistic parser.
tech,2-2-J05-1003,bq	The base <term> parser </term> produces a set of <term> candidate parses </term> for each input <term> sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#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,7-2-J05-1003,bq	The base <term> parser </term> produces a set of <term> candidate parses </term> for each input <term> sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </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.
other,12-2-J05-1003,bq	The base <term> parser </term> produces a set of <term> candidate parses </term> for each input <term> sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#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,16-2-J05-1003,bq	The base <term> parser </term> produces a set of <term> candidate parses </term> for each input <term> sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#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,21-2-J05-1003,bq	The base <term> parser </term> produces a set of <term> candidate parses </term> for each input <term> sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#8684 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initialranking of these parses.
other,24-2-J05-1003,bq	The base <term> parser </term> produces a set of <term> candidate parses </term> for each input <term> sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#8687 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of theseparses.
tech,2-3-J05-1003,bq	A second <term> model </term> then attempts to improve upon this initial <term> ranking </term> , using additional <term> features </term> of the <term> tree </term> as evidence .	#8691 A secondmodel then attempts to improve upon this initial ranking, using additional features of the tree as evidence.
other,10-3-J05-1003,bq	A second <term> model </term> then attempts to improve upon this initial <term> ranking </term> , using additional <term> features </term> of the <term> tree </term> as evidence .	#8699 A second model then attempts to improve upon this initialranking, using additional features of the tree as evidence.
other,14-3-J05-1003,bq	A second <term> model </term> then attempts to improve upon this initial <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	A second <term> model </term> then attempts to improve upon this initial <term> ranking </term> , using additional <term> features </term> of the <term> tree </term> as evidence .	#8706 A second model then attempts to improve upon this initial ranking, using additional features of thetree as evidence.
tech,4-4-J05-1003,bq	The strength of our <term> approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary set of <term> features </term> , without concerns about how these <term> features </term> interact or overlap and without the need to define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term> into account .	#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,10-4-J05-1003,bq	The strength of our <term> approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary set of <term> features </term> , without concerns about how these <term> features </term> interact or overlap and without the need to define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term> into account .	#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,19-4-J05-1003,bq	The strength of our <term> approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary set of <term> features </term> , without concerns about how these <term> features </term> interact or overlap and without the need to define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term> into account .	#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,26-4-J05-1003,bq	The strength of our <term> approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary set of <term> features </term> , without concerns about how these <term> features </term> interact or overlap and without the need to define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term> into account .	#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.
other,37-4-J05-1003,bq	The strength of our <term> approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary set of <term> features </term> , without concerns about how these <term> features </term> interact or overlap and without the need to define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term> into account .	#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,40-4-J05-1003,bq	The strength of our <term> approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary set of <term> features </term> , without concerns about how these <term> features </term> interact or overlap and without the need to define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term> into account .	#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,45-4-J05-1003,bq	The strength of our <term> approach </term> is that it allows a <term> tree </term> to be represented as an arbitrary set of <term> features </term> , without concerns about how these <term> features </term> interact or overlap and without the need to define a <term> derivation </term> or a <term> generative model </term> which takes these <term> features </term> into account .	#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,4-5-J05-1003,bq	We introduce a new <term> method </term> for the <term> reranking task </term> , based on the <term> boosting approach </term> to <term> ranking problems </term> described in <term> Freund et al. ( 1998 ) </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,7-5-J05-1003,bq	We introduce a new <term> method </term> for the <term> reranking task </term> , based on the <term> boosting approach </term> to <term> ranking problems </term> described in <term> Freund et al. ( 1998 ) </term> .	#8766 We introduce a new method for thereranking task, based on the boosting approach to ranking problems described in Freund et al. (1998).


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