converted to numerical features using the standard technique of binarization , and we split values of the FEATS field into its atomic components .4 For some languages , we divide the training data into smaller sets , based on some feature s ( normally the CPOS or POS of the next input token ) , which may reduce training times without a significant loss in accuracy ( Yamada and Matsumoto , 2003 ) .
statistical dependency analysis with support vector machines
H Yamada Y Matsumoto
3We also ran preliminary experiments with memory-based learning but found that this gave consistently lower accuracy . converted to numerical features using the standard technique of binarization , and we split values of the FEATS field into its atomic components .4 For some languages , we divide the training data into smaller sets , based on some feature s ( normally the CPOS or POS of the next input token ) , which may reduce training times without a significant loss in accuracy ( Yamada and Matsumoto , 2003 ) . To avoid too small training sets , we pool together categories that have a frequency below a certain threshold t.
converted to numerical features using the standard technique of binarization , and we split values of the FEATS field into its atomic components .4 For some languages , we divide the training data into smaller sets , based on some feature s ( normally the CPOS or POS of the next input token ) , which may reduce training times without a significant loss in accuracy ( @@CITATION ) .