| model,20-5-H92-1026,ak | In head-to-head tests against one of the best existing <term> robust probabilistic parsing models </term> , which we call <term> P-CFG </term> , the <term> HBG model </term> significantly outperforms <term> P-CFG </term> , increasing the <term> parsing accuracy rate </term> from 60 % to 75 % , a 37 % reduction in error . | #23999 In head-to-head tests against one of the best existing robust probabilistic parsing models, which we call P-CFG, theHBG model significantly outperforms P-CFG, increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error. | |
| model,24-5-H92-1026,ak | In head-to-head tests against one of the best existing <term> robust probabilistic parsing models </term> , which we call <term> P-CFG </term> , the <term> HBG model </term> significantly outperforms <term> P-CFG </term> , increasing the <term> parsing accuracy rate </term> from 60 % to 75 % , a 37 % reduction in error . | #24003 In head-to-head tests against one of the best existing robust probabilistic parsing models, which we call P-CFG, the HBG model significantly outperformsP-CFG, increasing the parsing accuracy rate from 60% to 75%, a 37% reduction in error. | |
| measure(ment),28-5-H92-1026,ak | In head-to-head tests against one of the best existing <term> robust probabilistic parsing models </term> , which we call <term> P-CFG </term> , the <term> HBG model </term> significantly outperforms <term> P-CFG </term> , increasing the <term> parsing accuracy rate </term> from 60 % to 75 % , a 37 % reduction in error . | #24007 In head-to-head tests against one of the best existing robust probabilistic parsing models, which we call P-CFG, the HBG model significantly outperforms P-CFG, increasing theparsing accuracy rate from 60% to 75%, a 37% reduction in error. |
