C02-1101 tagging using the corpus with revision learning . The distribution of the obtained
P02-1063 Experiments of English POS tagging with revision learning ( RL ) are performed on the Penn
N07-1049 Massimiliano Abstract We present a revision learning model for improving the accuracy
C02-1101 POS tag class . In such way , revision learning makes a model of SVMs to revise
P02-1063 Revision Learning We introduced revision learning for multi-class classification
P02-1063 Japanese morphological analysis with revision learning . Section 5 shows experimental
P02-1063 training data generated in the revision learning can be much smaller than that
P02-1063 unknown words . One advantage of revision learning is its small computational cost
P02-1063 unknown words are improved through revision learning . However , revision learning
P02-1063 Japanese morphological analysis with revision learning . Section 6 discusses related
P02-1063 non-segmented languages directly . When revision learning is used , all the measures are
P02-1063 Japanese morphological analysis with revision learning . 5.1 Experiments of English
P02-1063 4 Morphological Analysis with Revision Learning We introduced revision learning
P02-1063 node , and repeat the following revision learning process backward until the beginning
P02-1063 revision learning . However , revision learning could not surpass the one-versus-rest
P02-1063 prob - lem . Section 3 introduces revision learning , and discusses how to combine
N07-1049 mistakes of the base parser itself . Revision learning is performed with a discriminative
P02-1063 section , we describe how to apply revision learning to Japanese morphological analysis
P02-1063 several useful discussions . <title> Revision Learning and its Application to Part-of-Speech
C02-1101 Learning for POS tagging We use a revision learning method ( Nakagawa et al. , 2002
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