| other,13-3-P05-1046,ak | suitable <term> generative model </term> for <term> | field structured text | </term> , general <term> unsupervised HMM learning | #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,21-5-P05-1046,ak | </term> comparable to those attained by <term> | supervised methods | </term> on 50 <term> labeled examples </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 | <term> supervised methods </term> on 50 <term> | labeled examples | </term> , and that <term> semi-supervised methods | #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 | <term> labeled examples </term> , and that <term> | semi-supervised methods | </term> can make good use of small amounts | #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. | |
| tech,5-1-P05-1046,ak | The applicability of many current <term> | information extraction techniques | </term> is severely limited by the need for | #9021 The applicability of many currentinformation extraction techniques is severely limited by the need for supervised training data. | |
| tech,18-3-P05-1046,ak | field structured text </term> , general <term> | unsupervised HMM learning | </term> fails to learn useful structure in | #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. | |
| lr,40-5-P05-1046,ak | make good use of small amounts of <term> | labeled data | </term> . We directly investigate a subject | #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. | |
| lr,15-1-P05-1046,ak | is severely limited by the need for <term> | supervised training data | </term> . We demonstrate that for certain | #9031 The applicability of many current information extraction techniques is severely limited by the need forsupervised training data. | |
| other,30-3-P05-1046,ak | useful structure in either of our <term> | domains | </term> . However , one can dramatically | #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. | |
| lr,14-5-P05-1046,ak | attain <term> accuracies </term> with 400 <term> | unlabeled examples | </term> comparable to those attained by <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,10-3-P05-1046,ak | ( HMMs ) </term> provide a suitable <term> | generative model | </term> for <term> field structured text </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. | |
| tech,7-5-P05-1046,ak | . In both domains , we found that <term> | unsupervised methods | </term> can attain <term> accuracies </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 | unsupervised methods </term> can attain <term> | accuracies | </term> with 400 <term> unlabeled examples </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. | |
| tech,1-3-P05-1046,ak | primarily unsupervised fashion . Although <term> | hidden Markov models ( HMMs ) | </term> provide a suitable <term> generative | #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. |
