% changing the training data to 15000

Learning a rule sequence...
Loading data: data/wsj_15000 ... done! Size is 14988.
Loading algorithm: algorithms/brill ... done!
Loading templates: templates/test_templates ... done!
20    1.00   tag:'VBP'>'VB' <- tag:'MD'@[-1,-2]
13    1.00   tag:'NN'>'VB' <- tag:'MD'@[-1]
13    1.00   tag:'VBP'>'VB' <- tag:'TO'@[-1]
12    0.88   tag:'VB'>'VBP' <- tag:'NNS'@[-1]
12    1.00   tag:'VBN'>'VBD' <- tag:'PP'@[-1]
10    1.00   tag:'IN'>'DT' <- wd:that@[0] & tag:'IN'@[-1]
10    0.78   tag:'VB'>'NN' <- tag:'DT'@[-1,-2]
10    1.00   tag:'VBN'>'VBD' <- tag:'NP'@[-1]
9     1.00   tag:'IN'>'WDT' <- tag:'MD'@[1]
8     0.83   tag:'\''>'POS' <- tag:'NNS'@[-1]
8     0.90   tag:'IN'>'WDT' <- tag:'VBD'@[1]
8     1.00   tag:'IN'>'RB' <- wd:much@[1]
12 rule(s) for feature(s) [tag]
Testing the learned rule sequence...
Loading templates: templates/test_templates ... done!
Loading data: data/wsj_test ... done! Size is 9625.

DATA STATISTICS:

            Corpus Size: 9625
         Number of Tags: 9625
 Number of Correct Tags: 9228
       Number of Errors: 397
                 Recall: 95.9% 
              Precision: 95.9%
                F-Score: 95.9%
Number of Tags per Word: 1.000

Applied 12 rule(s) for feature(s) [tag] in 0.016 seconds

DATA STATISTICS:

            Corpus Size: 9625
         Number of Tags: 9625
 Number of Correct Tags: 9286
       Number of Errors: 339
                 Recall: 96.5% 
              Precision: 96.5%
                F-Score: 96.5%
Number of Tags per Word: 1.000

Saving the rule sequence(s) in file 'rules/test.pl'.
Generating data for the Error Browser...
Load (or reload) the file "error_data.html"
into a HTML browser to view error data.
Finished!
