| 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 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> | #12187 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 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 | #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 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> | #12202 It 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> | #12208 It 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> | #12237 Finally, 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 | #12246 Finally, 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. |
