Concordance

Query P05-1046 14 (448.2 per million)

tech,5-1-P05-1046,ak	The applicability of many current <term> information extraction techniques </term> is severely limited by the need for <term> supervised training data </term> .	#9021 The applicability of many currentinformation extraction techniques is severely limited by the need for supervised training data.
lr,15-1-P05-1046,ak	The applicability of many current <term> information extraction techniques </term> is severely limited by the need for <term> supervised training data </term> .	#9031 The applicability of many current information extraction techniques is severely limited by the need forsupervised training data.
tech,1-3-P05-1046,ak	Although <term> hidden Markov models ( HMMs ) </term> provide a suitable <term> generative model </term> for <term> field structured text </term> , general <term> unsupervised HMM learning </term> fails to learn useful structure in either of our <term> domains </term> .	#9072 Althoughhidden Markov models ( HMMs ) provide a suitable generative model for field structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains.
tech,10-3-P05-1046,ak	Although <term> hidden Markov models ( HMMs ) </term> provide a suitable <term> generative model </term> for <term> field structured text </term> , general <term> unsupervised HMM learning </term> fails to learn useful structure in either of our <term> domains </term> .	#9081 Although hidden Markov models (HMMs) provide a suitablegenerative model for field structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains.
other,13-3-P05-1046,ak	Although <term> hidden Markov models ( HMMs ) </term> provide a suitable <term> generative model </term> for <term> field structured text </term> , general <term> unsupervised HMM learning </term> fails to learn useful structure in either of our <term> domains </term> .	#9084 Although hidden Markov models (HMMs) provide a suitable generative model forfield structured text, general unsupervised HMM learning fails to learn useful structure in either of our domains.
tech,18-3-P05-1046,ak	Although <term> hidden Markov models ( HMMs ) </term> provide a suitable <term> generative model </term> for <term> field structured text </term> , general <term> unsupervised HMM learning </term> fails to learn useful structure in either of our <term> domains </term> .	#9089 Although hidden Markov models (HMMs) provide a suitable generative model for field structured text, generalunsupervised HMM learning fails to learn useful structure in either of our domains.
other,30-3-P05-1046,ak	Although <term> hidden Markov models ( HMMs ) </term> provide a suitable <term> generative model </term> for <term> field structured text </term> , general <term> unsupervised HMM learning </term> fails to learn useful structure in either of our <term> domains </term> .	#9101 Although hidden Markov models (HMMs) provide a suitable generative model for field structured text, general unsupervised HMM learning fails to learn useful structure in either of ourdomains.
tech,7-5-P05-1046,ak	In both domains , we found that <term> unsupervised methods </term> can attain <term> accuracies </term> with 400 <term> unlabeled examples </term> comparable to those attained by <term> supervised methods </term> on 50 <term> labeled examples </term> , and that <term> semi-supervised methods </term> can make good use of small amounts of <term> labeled data </term> .	#9132 In both domains, we found thatunsupervised methods can attain accuracies with 400 unlabeled examples comparable to those attained by supervised methods on 50 labeled examples, and that semi-supervised methods can make good use of small amounts of labeled data.
measure(ment),11-5-P05-1046,ak	In both domains , we found that <term> unsupervised methods </term> can attain <term> accuracies </term> with 400 <term> unlabeled examples </term> comparable to those attained by <term> supervised methods </term> on 50 <term> labeled examples </term> , and that <term> semi-supervised methods </term> can make good use of small amounts of <term> labeled data </term> .	#9136 In both domains, we found that unsupervised methods can attainaccuracies with 400 unlabeled examples comparable to those attained by supervised methods on 50 labeled examples, and that semi-supervised methods can make good use of small amounts of labeled data.
lr,14-5-P05-1046,ak	In both domains , we found that <term> unsupervised methods </term> can attain <term> accuracies </term> with 400 <term> unlabeled examples </term> comparable to those attained by <term> supervised methods </term> on 50 <term> labeled examples </term> , and that <term> semi-supervised methods </term> can make good use of small amounts of <term> labeled data </term> .	#9139 In both domains, we found that unsupervised methods can attain accuracies with 400unlabeled examples comparable to those attained by supervised methods on 50 labeled examples, and that semi-supervised methods can make good use of small amounts of labeled data.
tech,21-5-P05-1046,ak	In both domains , we found that <term> unsupervised methods </term> can attain <term> accuracies </term> with 400 <term> unlabeled examples </term> comparable to those attained by <term> supervised methods </term> on 50 <term> labeled examples </term> , and that <term> semi-supervised methods </term> can make good use of small amounts of <term> labeled data </term> .	#9146 In both domains, we found that unsupervised methods can attain accuracies with 400 unlabeled examples comparable to those attained bysupervised methods on 50 labeled examples, and that semi-supervised methods can make good use of small amounts of labeled data.
lr,25-5-P05-1046,ak	In both domains , we found that <term> unsupervised methods </term> can attain <term> accuracies </term> with 400 <term> unlabeled examples </term> comparable to those attained by <term> supervised methods </term> on 50 <term> labeled examples </term> , and that <term> semi-supervised methods </term> can make good use of small amounts of <term> labeled data </term> .	#9150 In both domains, we found that unsupervised methods can attain accuracies with 400 unlabeled examples comparable to those attained by supervised methods on 50labeled examples, and that semi-supervised methods can make good use of small amounts of labeled data.
tech,30-5-P05-1046,ak	In both domains , we found that <term> unsupervised methods </term> can attain <term> accuracies </term> with 400 <term> unlabeled examples </term> comparable to those attained by <term> supervised methods </term> on 50 <term> labeled examples </term> , and that <term> semi-supervised methods </term> can make good use of small amounts of <term> labeled data </term> .	#9155 In both domains, we found that unsupervised methods can attain accuracies with 400 unlabeled examples comparable to those attained by supervised methods on 50 labeled examples, and thatsemi-supervised methods can make good use of small amounts of labeled data.
lr,40-5-P05-1046,ak	In both domains , we found that <term> unsupervised methods </term> can attain <term> accuracies </term> with 400 <term> unlabeled examples </term> comparable to those attained by <term> supervised methods </term> on 50 <term> labeled examples </term> , and that <term> semi-supervised methods </term> can make good use of small amounts of <term> labeled data </term> .	#9165 In both domains, we found that unsupervised methods can attain accuracies with 400 unlabeled examples comparable to those attained by supervised methods on 50 labeled examples, and that semi-supervised methods can make good use of small amounts oflabeled data.


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