| tech,11-1-J05-1003,ak | which rerank the output of an existing <term> | probabilistic parser | </term> . The <term> base parser </term> produces | #8025 This article considers approaches which rerank the output of an existingprobabilistic parser. | |
| tech,1-2-J05-1003,ak | <term> probabilistic parser </term> . The <term> | base parser | </term> produces a set of <term> candidate | #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 | base parser </term> produces a set of <term> | candidate parses | </term> for each <term> input sentence </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 | <term> candidate parses </term> for each <term> | input sentence | </term> , with associated <term> probabilities | #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 | input sentence </term> , with associated <term> | probabilities | </term> that define an initial <term> ranking | #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 | probabilities </term> that define an initial <term> | ranking | </term> of these <term> parses </term> . A second | #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 | initial <term> ranking </term> of these <term> | parses | </term> . A second <term> model </term> then | #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 | these <term> parses </term> . A second <term> | model | </term> then attempts to improve upon this | #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 | attempts to improve upon this initial <term> | ranking | </term> , using additional <term> features </term> | #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 | <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 | additional <term> features </term> of the <term> | tree | </term> as evidence . The strength of our | #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 | of our approach is that it allows a <term> | tree | </term> to be represented as an arbitrary | #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 | represented as an arbitrary set of <term> | features | </term> , without concerns about how these | #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 | , without concerns about how these <term> | features | </term> interact or overlap and without the | #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 | overlap and without the need to define a <term> | derivation | </term> or a <term> generative model </term> | #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 | define a <term> derivation </term> or a <term> | generative model | </term> which takes these <term> features </term> | #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 | generative model </term> which takes these <term> | features | </term> into account . We introduce a new | #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 | #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 | 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). | |
| lr,8-6-J05-1003,ak | boosting method </term> to parsing the <term> | Wall Street Journal treebank | </term> . The method combined the <term> log-likelihood | #8159 We apply the boosting method to parsing theWall Street Journal treebank. |
