#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 arecall of 98%.
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 arecall of 49% in the task of placing commas.
tech,15-1-P06-2001,ak
learning techniques
</term>
to build a
<term>
comma checker
</term>
to be integrated in a
<term>
grammar
#12152In this paper, we describe the research using machine learning techniques to build acomma checker to be integrated in a grammar checker for Basque.
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>
of
#12202It also gets aprecision of 70% and a recall of 49% in the task of placing commas.
tech,22-1-P06-2001,ak
checker
</term>
to be integrated in a
<term>
grammar checker
</term>
for
<term>
Basque
</term>
. After several
#12159In this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in agrammar checker for Basque.
measure(ment),23-2-P06-2001,ak
not placing
<term>
commas
</term>
with a
<term>
precision
</term>
of 96 % and a
<term>
recall
</term>
of
#12187After several experiments, and trained with a little corpus of 100,000 words, the system guesses correctly not placing commas with aprecision of 96% and a recall of 98%.
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 biggercorpus written by one unique author.
other,25-1-P06-2001,ak
in a
<term>
grammar checker
</term>
for
<term>
Basque
</term>
. After several experiments , and
#12162In this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in a grammar checker forBasque.
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 homogeneouscorpus to train, that is, a bigger corpus written by one unique author.
lr,9-2-P06-2001,ak
experiments , and trained with a little
<term>
corpus
</term>
of 100,000 words , the system guesses
#12173After several experiments, and trained with a littlecorpus of 100,000 words, the system guesses correctly not placing commas with a precision of 96% and a recall of 98%.
other,20-2-P06-2001,ak
system guesses correctly not placing
<term>
commas
</term>
with a
<term>
precision
</term>
of 96
#12184After several experiments, and trained with a little corpus of 100,000 words, the system guesses correctly not placingcommas with a precision of 96% and a recall of 98%.
other,19-3-P06-2001,ak
</term>
of 49 % in the task of placing
<term>
commas
</term>
. Finally , we have shown that these
#12217It also gets a precision of 70% and a recall of 49% in the task of placingcommas.
tech,9-1-P06-2001,ak
paper , we describe the research using
<term>
machine learning techniques
</term>
to build a
<term>
comma checker
</term>
#12146In this paper, we describe the research usingmachine learning techniques to build a comma checker to be integrated in a grammar checker for Basque.