#12173After several experiments, and trained with a little corpus of 100,000 words, the system guesses correctly not placing commas with a precision of 96% and a recall of 98%.
measure(ment),23-2-P06-2001,ak
placing
<term>
commas
</term>
with a
<term>
precision
</term>
of 96 % and a
<term>
recall
</term>
#12187After several experiments, and trained with a little corpus of 100,000 words, the system guesses correctly not placing commas with a precision of 96% and a recall of 98%.
measure(ment),29-2-P06-2001,ak
<term>
precision
</term>
of 96 % and a
<term>
recall
</term>
of 98 % . It also gets a
<term>
precision
#12193After several experiments, and trained with a little corpus of 100,000 words, the system guesses correctly not placing commas with a precision of 96% and a recall of 98%.
measure(ment),4-3-P06-2001,ak
recall
</term>
of 98 % . It also gets a
<term>
precision
</term>
of 70 % and a
<term>
recall
</term>
#12202It also gets a precision of 70% and a recall of 49% in the task of placing commas.
measure(ment),10-3-P06-2001,ak
<term>
precision
</term>
of 70 % and a
<term>
recall
</term>
of 49 % in the task of placing
<term>
#12208It also gets a precision of 70% and a recall of 49% in the task of placing commas.
lr,18-4-P06-2001,ak
using a bigger and a more homogeneous
<term>
corpus
</term>
to train , that is , a bigger
<term>
#12237Finally, we have shown that these results can be improved using a bigger and a more homogeneous corpus to train, that is, a bigger corpus written by one unique author.
lr,27-4-P06-2001,ak
</term>
to train , that is , a bigger
<term>
corpus
</term>
written by one unique author . This
#12246Finally, we have shown that these results can be improved using a bigger and a more homogeneous corpus to train, that is, a bigger corpus written by one unique author.