| measure(ment),7-3-H01-1070,ak | algorithm </term> reported more than 99 % <term> | accuracy | </term> in both <term> language identification | #1284 Our algorithm reported more than 99%accuracy in both language identification and key prediction. | |
| measure(ment),16-3-P01-1004,ak | bigrams </term> produces a <term> retrieval | accuracy | </term> superior to any of the tested <term> | #1548 Over two distinct datasets, we find that indexing according to simple character bigrams produces a retrieval accuracy superior to any of the tested word N-gram models. | |
| measure(ment),21-4-P01-1004,ak | methods </term> in terms of <term> retrieval | accuracy | </term> , but much faster . We also provide | #1581 Further,in their optimum configuration, bag-of-words methods are shown to be equivalent to segment order-sensitive methods in terms of retrieval accuracy, but much faster. | |
| measure(ment),1-4-N03-1001,ak | classifier </term> . The <term> classification | accuracy | </term> of the method is evaluated on three | #2293 The classification accuracy of the method is evaluated on three different spoken language system domains. | |
| measure(ment),12-2-N03-1033,ak | <term> tagger </term> gives a 97.24 % <term> | accuracy | </term> on the <term> Penn Treebank WSJ </term> | #2992 Using these ideas together, the resulting tagger gives a 97.24%accuracy on the Penn Treebank WSJ, an error reduction of 4.4% on the best previous single automatically learned tagging result. | |
| measure(ment),11-3-N03-3010,ak | understanding </term> and have a high <term> | accuracy | </term> but little robustness and flexibility | #3528 FSM provides two strategies for language understanding and have a highaccuracy but little robustness and flexibility. | |
| measure(ment),17-4-P03-1031,ak | progresses , the <term> discourse understanding | accuracy | </term> can be improved . This paper proposes | #4213 By holding multiple candidates for understanding results and resolving the ambiguity as the dialogue progresses, the discourse understanding accuracy can be improved. | |
| measure(ment),3-5-P03-1033,ak | obtained reasonable <term> classification | accuracy | </term> for all <term> dimensions </term> . <term> | #4377 We obtained reasonable classification accuracy for all dimensions. | |
| measure(ment),3-5-P03-1051,ak | </term> . To improve the <term> segmentation | accuracy | </term> , we use an <term> unsupervised algorithm | #4712 To improve the segmentation accuracy, we use an unsupervised algorithm for automatically acquiring new stems from a 155 million word unsegmented corpus, and re-estimate the model parameters with the expanded vocabulary and training corpus. | |
| measure(ment),10-6-P03-1051,ak | </term> achieves around 97 % <term> exact match | accuracy | </term> on a <term> test corpus </term> containing | #4757 The resulting Arabic word segmentation system achieves around 97% exact match accuracy on a test corpus containing 28,449 word tokens. | |
| measure(ment),11-4-P03-1058,ak | <term> SENSEVAL-2 nouns </term> , the <term> | accuracy | difference </term> between the two approaches | #4882 On a subset of the most difficult SENSEVAL-2 nouns, theaccuracy difference between the two approaches is only 14.0%, and the difference could narrow further to 6.5% if we disregard the advantage that manually sense-tagged data have in their sense coverage. | |
| measure(ment),23-3-H05-1095,ak | <term> maximization </term> of <term> translation | accuracy | </term> , as measured with the <term> NIST | #5638 A statistical translation model is also presented that deals such phrases, as well as a training method based on the maximization of translation accuracy, as measured with the NIST evaluation metric. | |
| measure(ment),4-4-I05-2021,ak | </term> . Surprisingly however , the <term> WSD | accuracy | </term> of <term> SMT models </term> has never | #6442 Surprisingly however, the WSD accuracy of SMT models has never been evaluated and compared with that of the dedicated WSD models. | |
| measure(ment),6-5-I05-2021,ak | controlled experiments showing the <term> WSD | accuracy | </term> of current typical <term> SMT models | #6467 We present controlled experiments showing the WSD accuracy of current typical SMT models to be significantly lower than that of all the dedicated WSD models considered. | |
| measure(ment),20-6-I05-2044,ak | , showing improvement of <term> dependency | accuracy | </term> by 10.08 % . <term> Statistical machine | #6732 In experimental evaluation, our proposed method outperforms previous shift-reduce dependency parsers for the Chine language, showing improvement of dependency accuracy by 10.08%. | |
| tech,7-5-I05-5003,ak | improvement in <term> paraphrase classification | accuracy | </term> over all of the other models used | #7479 Our technique gives a substantial improvement in paraphrase classification accuracy over all of the other models used in the experiments. | |
| measure(ment),11-4-I05-5009,ak | proposed method achieves almost 60 % <term> | accuracy | </term> and that there is not a large performance | #7759 Experimental results showed that the proposed method achieves almost 60%accuracy and that there is not a large performance difference between the two models. | |
| measure(ment),8-5-I05-5009,ak | revealed an <term> upper bound </term> of <term> | accuracy | </term> of 77 % with the method when using | #7782 The results also revealed an upper bound ofaccuracy of 77% with the method when using only topic information. | |
| measure(ment),10-4-P05-1018,ak | model achieves significantly higher <term> | accuracy | </term> than a state-of-the-art <term> coherence | #8662 Our experiments demonstrate that the induced model achieves significantly higheraccuracy than a state-of-the-art coherence model. | |
| measure(ment),5-2-P05-1039,ak | corpus </term> . In addition to the high <term> | accuracy | </term> of the model , the use of <term> smoothing | #8990 In addition to the highaccuracy of the model, the use of smoothing in an unlexicalized parser allows us to better examine the interplay between smoothing and parsing results. |
