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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),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),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.
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),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),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),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.
	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),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),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),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),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),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.
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),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),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),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),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.


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