| measure(ment),7-5-I05-2021,bq | experiments showing the <term> WSD </term><term> | accuracy | </term> of current typical <term> SMT models | #7924 We present controlled experiments showing the WSDaccuracy of current typical SMT models to be significantly lower than that of all the dedicated WSD models considered. |
| measure(ment),7-5-I05-5003,bq | improvement in <term> paraphrase classification | accuracy | </term> over all of the other <term> models | #8429 Our technique gives a substantial improvement in paraphrase classification accuracy over all of the other models used in the experiments. |
| measure(ment),13-3-C92-1055,bq | adjusting the parameters to maximize the <term> | accuracy | rate </term> directly . To make the proposed | #17876 The proposed method remedies these problems by adjusting the parameters to maximize theaccuracy rate directly. |
| measure(ment),1-6-C92-1055,bq | has been observed in the test . The <term> | accuracy | rate </term> of <term> syntactic disambiguation | #17927 Theaccuracy rate of syntactic disambiguation is raised from 46.0% to 60.62% by using this novel approach. |
| measure(ment),21-4-H92-1017,bq | </term> to improving <term> OCR </term><term> | accuracy | </term> . We describe a <term> generative probabilistic | #18891 Finally, we briefly describe an experiment which we have done in extending the n-best speech/language integration architecture to improving OCRaccuracy. |
| measure(ment),28-5-H92-1026,bq | P-CFG </term> , increasing the <term> parsing | accuracy | </term> rate from 60 % to 75 % , a 37 % reduction | #19036 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 the parsing accuracy rate from 60% to 75%, a 37% reduction in error. |
| measure(ment),18-7-A94-1007,bq | system </term> , and provided about 75 % <term> | accuracy | </term> in the practical <term> translation | #19876 This model was practically implemented and incorporated into the English-Japanese MT system, and provided about 75%accuracy in the practical translation use. |
| measure(ment),13-4-H94-1014,bq | show a 7 % improvement in <term> recognition | accuracy | </term> with the <term> mixture trigram models | #21277 Using the BU recognition system, experiments show a 7% improvement in recognition accuracy with the mixture trigram models as compared to using a trigram model. |
