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Query J05-1003 44 >
Subparts filter 44 (1,408.7 per million)

tech,11-1-J05-1003,ak	This article considers approaches which rerank the output of an existing <term> probabilistic parser </term> .	#8025 This article considers approaches which rerank the output of an existingprobabilistic parser.
tech,1-2-J05-1003,ak	The <term> base parser </term> produces a set of <term> candidate parses </term> for each <term> input sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#8029 Thebase parser 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,ak	The <term> base parser </term> produces a set of <term> candidate parses </term> for each <term> input sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#8035 The base parser produces a set ofcandidate parses for each input sentence, with associated probabilities that define an initial ranking of these parses.
other,11-2-J05-1003,ak	The <term> base parser </term> produces a set of <term> candidate parses </term> for each <term> input sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#8039 The base parser produces a set of candidate parses for eachinput sentence, with associated probabilities that define an initial ranking of these parses.
other,16-2-J05-1003,ak	The <term> base parser </term> produces a set of <term> candidate parses </term> for each <term> input sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#8044 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,ak	The <term> base parser </term> produces a set of <term> candidate parses </term> for each <term> input sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#8049 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,ak	The <term> base parser </term> produces a set of <term> candidate parses </term> for each <term> input sentence </term> , with associated <term> probabilities </term> that define an initial <term> ranking </term> of these <term> parses </term> .	#8052 The base parser produces a set of candidate parses for each input sentence, with associated probabilities that define an initial ranking of theseparses.
model,2-3-J05-1003,ak	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 .	#8056 A secondmodel then attempts to improve upon this initial ranking, using additional features of the tree as evidence.
other,10-3-J05-1003,ak	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 .	#8064 A second model then attempts to improve upon this initialranking, using additional features of the tree as evidence.
other,14-3-J05-1003,ak	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 .	#8068 A second model then attempts to improve upon this initial ranking, using additionalfeatures of the tree as evidence.
other,17-3-J05-1003,ak	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 .	#8071 A second model then attempts to improve upon this initial ranking, using additional features of thetree as evidence.
other,10-4-J05-1003,ak	The strength of our approach 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 .	#8085 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,ak	The strength of our approach 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 .	#8094 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,ak	The strength of our approach 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 .	#8101 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,ak	The strength of our approach 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 .	#8112 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.
model,40-4-J05-1003,ak	The strength of our approach 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 .	#8115 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,ak	The strength of our approach 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 .	#8120 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,7-5-J05-1003,ak	We introduce a new method for the <term> reranking task </term> , based on the <term> boosting approach to ranking problems </term> described in Freund et al. ( 1998 ) .	#8131 We introduce a new method for thereranking task, based on the boosting approach to ranking problems described in Freund et al. (1998).
tech,13-5-J05-1003,ak	We introduce a new method for the <term> reranking task </term> , based on the <term> boosting approach to ranking problems </term> described in Freund et al. ( 1998 ) .	#8137 We introduce a new method for the reranking task, based on theboosting approach to ranking problems described in Freund et al. (1998).
tech,3-6-J05-1003,ak	We apply the <term> boosting method </term> to parsing the <term> Wall Street Journal treebank </term> .	#8154 We apply theboosting method to parsing the Wall Street Journal treebank.


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