Concordance

Query accuracy 28 (1,305.4 per million)

measure(ment),7-3-H01-1070,bq	Our <term> algorithm </term> reported more than 99 % <term> accuracy </term> in both <term> language identification </term> and <term> key prediction </term> .	#1284 Our algorithm reported more than 99%accuracy in both language identification and key prediction.
measure(ment),16-3-P01-1004,bq	Over two distinct <term> datasets </term> , we find that <term> indexing </term> according to simple <term> character bigrams </term> produces a <term> retrieval accuracy </term> superior to any of the tested <term> word N-gram models </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	Further , in their optimum <term> configuration </term> , <term> bag-of-words methods </term> are shown to be equivalent to <term> segment order-sensitive methods </term> in terms of <term> retrieval accuracy </term> , but much faster .	#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	The <term> classification accuracy </term> of the <term> method </term> is evaluated on three different <term> spoken language system domains </term> .	#2292 The classification accuracy of the method is evaluated on three different spoken language system domains.
measure(ment),12-2-N03-1033,bq	Using these ideas together , the resulting <term> tagger </term> gives a 97.24 % <term> accuracy </term> on the <term> Penn Treebank WSJ </term> , an <term> error reduction </term> of 4.4 % on the best previous single automatically learned <term> tagging </term> result .	#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.
	<term> FSM </term> provides two strategies for <term> 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	By holding multiple <term> candidates </term> for <term> understanding </term> results and resolving the <term> ambiguity </term> as the <term> dialogue </term> progresses , the <term> discourse understanding accuracy </term> can be improved .	#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	We obtained reasonable <term> classification accuracy </term> for all dimensions .	#4375 We obtained reasonable classification accuracy for all dimensions.
measure(ment),4-5-P03-1051,bq	To improve the <term> segmentation </term><term> accuracy </term> , we use an <term> unsupervised algorithm </term> for automatically acquiring new <term> stems </term> from a 155 million <term> word </term><term> unsegmented corpus </term> , and re-estimate the <term> model parameters </term> with the expanded <term> vocabulary </term> and <term> training corpus </term> .	#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	The resulting <term> Arabic word segmentation system </term> achieves around 97 % <term> exact match accuracy </term> on a <term> test corpus </term> containing 28,449 <term> word tokens </term> .	#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	On a subset of the most difficult <term> SENSEVAL-2 nouns </term> , the <term> accuracy </term> difference between the two approaches is only 14.0 % , and the difference could narrow further to 6.5 % if we disregard the advantage that <term> manually sense-tagged data </term> have in their <term> sense coverage </term> .	#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	Observing that the quality of the <term> lexicon </term> greatly impacts the <term> accuracy </term> that can be achieved by the <term> algorithms </term> , we present a method of <term> HMM training </term> that improves <term> accuracy </term> when training of <term> lexical probabilities </term> is unstable .	#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	Observing that the quality of the <term> lexicon </term> greatly impacts the <term> accuracy </term> that can be achieved by the <term> algorithms </term> , we present a method of <term> HMM training </term> that improves <term> accuracy </term> when training of <term> lexical probabilities </term> is unstable .	#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	Our study reveals that the proposed method not only reduces an extensive system development effort but also improves the <term> transliteration accuracy </term> significantly .	#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	Testing the <term> lemma-based model </term> on the <term> Dutch SENSEVAL-2 test data </term> , we 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	This paper proposes a new methodology to improve the <term> accuracy </term> of a <term> term aggregation system </term> using each author 's text as a coherent <term> corpus </term> .	#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 </term> , showing that our approach is successful .	#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 .	#6781 Despite the successes of these systems,accuracy will always be imperfect.
measure(ment),23-3-H05-1095,bq	A <term> statistical translation model </term> is also presented that deals such <term> phrases </term> , as well as a <term> training method </term> based on the maximization of <term> translation accuracy </term> , as measured with the <term> NIST evaluation metric </term> .	#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 been evaluated and compared with that of the dedicated <term> WSD models </term> .	#7899 Surprisingly however, the WSDaccuracy of SMT models has never been evaluated and compared with that of the dedicated WSD models.


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