| measure(ment),7-3-H01-1070,bq | 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,bq | 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,bq | 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,bq | classifier </term> . The <term> classification | accuracy | </term> of the <term> method </term> is evaluated | #2292 The classification accuracy of the method is evaluated on three different spoken language system domains. |
| measure(ment),12-2-N03-1033,bq | <term> tagger </term> gives a 97.24 % <term> | accuracy | </term> on the <term> Penn Treebank WSJ </term> | #2991 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. |
| language understanding </term> and have a high | accuracy | but little robustness and flexibility . | #3527 FSM provides two strategies for language understanding and have a high accuracy but little robustness and flexibility. | |
| measure(ment),17-4-P03-1031,bq | progresses , the <term> discourse understanding | accuracy | </term> can be improved . This paper proposes | #4211 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,bq | obtained reasonable <term> classification | accuracy | </term> for all dimensions . <term> Dialogue | #4375 We obtained reasonable classification accuracy for all dimensions. |
| measure(ment),4-5-P03-1051,bq | improve the <term> segmentation </term><term> | accuracy | </term> , we use an <term> unsupervised algorithm | #4710 To improve the segmentationaccuracy, 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,bq | </term> achieves around 97 % <term> exact match | accuracy | </term> on a <term> test corpus </term> containing | #4755 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,bq | <term> SENSEVAL-2 nouns </term> , the <term> | accuracy | </term> difference between the two approaches | #4880 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),10-3-C04-1080,bq | <term> lexicon </term> greatly impacts the <term> | accuracy | </term> that can be achieved by the <term> | #5568 Observing that the quality of the lexicon greatly impacts theaccuracy that can be achieved by the algorithms, we present a method of HMM training that improves accuracy when training of lexical probabilities is unstable. |
| measure(ment),28-3-C04-1080,bq | <term> HMM training </term> that improves <term> | accuracy | </term> when training of <term> lexical probabilities | #5586 Observing that the quality of the lexicon greatly impacts the accuracy that can be achieved by the algorithms, we present a method of HMM training that improvesaccuracy when training of lexical probabilities is unstable. |
| measure(ment),19-5-C04-1103,bq | but also improves the <term> transliteration | accuracy | </term> significantly . The reality of <term> | #5839 Our study reveals that the proposed method not only reduces an extensive system development effort but also improves the transliteration accuracy significantly. |
| measure(ment),17-4-C04-1112,bq | achieve a significant increase in <term> | accuracy | </term> over the <term> wordform model </term> | #6072 Testing the lemma-based model on the Dutch SENSEVAL-2 test data, we achieve a significant increase inaccuracy over the wordform model. |
| measure(ment),9-2-C04-1116,bq | proposes a new methodology to improve the <term> | accuracy | </term> of a <term> term aggregation system | #6123 This paper proposes a new methodology to improve theaccuracy of a term aggregation system using each author's text as a coherent corpus. |
| measure(ment),5-5-C04-1116,bq | . Our proposed method improves the <term> | accuracy | </term> of our <term> term aggregation system | #6188 Our proposed method improves theaccuracy of our term aggregation system, showing that our approach is successful. |
| measure(ment),7-2-N04-4028,bq | Despite the successes of these systems , <term> | accuracy | </term> will always be imperfect . For many | #6781 Despite the successes of these systems,accuracy will always be imperfect. |
| measure(ment),23-3-H05-1095,bq | on the maximization of <term> translation | accuracy | </term> , as measured with the <term> NIST | #7394 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),5-4-I05-2021,bq | Surprisingly however , the <term> WSD </term><term> | accuracy | </term> of <term> SMT models </term> has never | #7899 Surprisingly however, the WSDaccuracy of SMT models has never been evaluated and compared with that of the dedicated WSD models. |
