| other,25-1-P06-2001,ak | in a <term> grammar checker </term> for <term> | Basque | </term> . After several experiments , and | #12162 In this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in a grammar checker forBasque. | |
| tech,15-1-P06-2001,ak | learning techniques </term> to build a <term> | comma checker | </term> to be integrated in a <term> grammar | #12152 In this paper, we describe the research using machine learning techniques to build acomma checker to be integrated in a grammar checker for Basque. | |
| other,20-2-P06-2001,ak | system guesses correctly not placing <term> | commas | </term> with a <term> precision </term> of 96 | #12184 After 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 | #12217 It also gets a precision of 70% and a recall of 49% in the task of placingcommas. | |
| lr,9-2-P06-2001,ak | experiments , and trained with a little <term> | corpus | </term> of 100,000 words , the system guesses | #12173 After 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%. | |
| lr,18-4-P06-2001,ak | using a bigger and a more homogeneous <term> | corpus | </term> to train , that is , a bigger <term> | #12237 Finally, 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,27-4-P06-2001,ak | </term> to train , that is , a bigger <term> | corpus | </term> written by one unique author . This | #12246 Finally, 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. | |
| tech,22-1-P06-2001,ak | checker </term> to be integrated in a <term> | grammar checker | </term> for <term> Basque </term> . After several | #12159 In this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in agrammar checker for Basque. | |
| tech,9-1-P06-2001,ak | paper , we describe the research using <term> | machine learning techniques | </term> to build a <term> comma checker </term> | #12146 In this paper, we describe the research usingmachine learning techniques to build a comma checker to be integrated in a grammar 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 | #12187 After 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%. | |
| 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 | #12202 It also gets aprecision of 70% and a recall of 49% in the task of placing commas. | |
| 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 | #12193 After 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> | #12208 It also gets a precision of 70% and arecall of 49% in the task of placing commas. |
