| 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). |
| other,16-5-J05-1003,bq | the <term> boosting approach </term> to <term> | ranking problems | </term> described in <term> Freund et al. ( | #8775 We introduce a new method for the reranking task, based on the boosting approach toranking problems described in Freund et al. (1998). |
| other,20-5-J05-1003,bq | ranking problems </term> described in <term> | Freund et al. ( 1998 ) | </term> . We apply the <term> boosting method | #8779 We introduce a new method for the reranking task, based on the boosting approach to ranking problems described inFreund et al. ( 1998 ). |
| 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,6-6-J05-1003,bq | the <term> boosting method </term> to <term> | parsing | </term> the <term> Wall Street Journal treebank | #8792 We apply the boosting method toparsing the Wall Street Journal treebank. |
| lr-prod,8-6-J05-1003,bq | method </term> to <term> parsing </term> the <term> | Wall Street Journal treebank | </term> . The <term> method </term> combined | #8794 We apply the boosting method to parsing theWall Street Journal treebank. |
| tech,1-7-J05-1003,bq | Street Journal treebank </term> . The <term> | method | </term> combined the <term> log-likelihood </term> | #8800 Themethod combined the log-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,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. |
| 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,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,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. |
| 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. |
| model,34-7-J05-1003,bq | were not included in the original <term> | model | </term> . The new <term> model </term> achieved | #8833 The method combined the log-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 originalmodel. |
| tech,2-8-J05-1003,bq | original <term> model </term> . The new <term> | model | </term> achieved 89.75 % <term> F-measure </term> | #8837 The newmodel achieved 89.75% F-measure, a 13% relative decrease in F-measure error over the baseline model’s score of 88.2%. |
| 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%. |
| measure(ment),14-8-J05-1003,bq | </term> , a 13 % relative decrease in <term> | F-measure | </term> error over the <term> baseline model | #8849 The new model achieved 89.75% F-measure, a 13% relative decrease inF-measure error over the baseline model’s score of 88.2%. |
| measure(ment),18-8-J05-1003,bq | <term> F-measure </term> error over the <term> | baseline model ’s score | </term> of 88.2 % . The article also introduces | #8853 The new model achieved 89.75% F-measure, a 13% relative decrease in F-measure error over thebaseline model ’s score of 88.2%. |
| tech,6-9-J05-1003,bq | The article also introduces a new <term> | algorithm | </term> for the <term> boosting approach </term> | #8867 The article also introduces a newalgorithm for the boosting approach which takes advantage of the sparsity of the feature space in the parsing data. |
| tech,9-9-J05-1003,bq | a new <term> algorithm </term> for the <term> | boosting approach | </term> which takes advantage of the <term> | #8870 The article also introduces a new algorithm for theboosting approach which takes advantage of the sparsity of the feature space in the parsing data. |
| other,16-9-J05-1003,bq | </term> which takes advantage of the <term> | sparsity of the feature space | </term> in the <term> parsing data </term> . | #8877 The article also introduces a new algorithm for the boosting approach which takes advantage of thesparsity of the feature space in the parsing data. |
