Concordance

Query P06-2001 15 >
Multilevel Sort 15 (699.3 per million)

lr,18-4-P06-2001,bq	Finally , we have shown that these results can be improved using a bigger and a more homogeneous <term> corpus </term> to train , that is , a bigger <term> corpus </term> written by one unique <term> author </term> .	#11300 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,bq	Finally , we have shown that these results can be improved using a bigger and a more homogeneous <term> corpus </term> to train , that is , a bigger <term> corpus </term> written by one unique <term> author </term> .	#11309 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.
lr,9-2-P06-2001,bq	After several experiments , and trained with a little <term> corpus </term> of 100,000 <term> words </term> , the system guesses correctly not placing <term> commas </term> with a <term> precision </term> of 96 % and a <term> recall </term> of 98 % .	#11236 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%.
measure(ment),10-3-P06-2001,bq	It also gets a <term> precision </term> of 70 % and a <term> recall </term> of 49 % in the task of placing <term> commas </term> .	#11271 It also gets a precision of 70% and arecall of 49% in the task of placing commas.
measure(ment),23-2-P06-2001,bq	After several experiments , and trained with a little <term> corpus </term> of 100,000 <term> words </term> , the system guesses correctly not placing <term> commas </term> with a <term> precision </term> of 96 % and a <term> recall </term> of 98 % .	#11250 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),29-2-P06-2001,bq	After several experiments , and trained with a little <term> corpus </term> of 100,000 <term> words </term> , the system guesses correctly not placing <term> commas </term> with a <term> precision </term> of 96 % and a <term> recall </term> of 98 % .	#11256 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),4-3-P06-2001,bq	It also gets a <term> precision </term> of 70 % and a <term> recall </term> of 49 % in the task of placing <term> commas </term> .	#11265 It also gets aprecision of 70% and a recall of 49% in the task of placing commas.
other,12-2-P06-2001,bq	After several experiments , and trained with a little <term> corpus </term> of 100,000 <term> words </term> , the system guesses correctly not placing <term> commas </term> with a <term> precision </term> of 96 % and a <term> recall </term> of 98 % .	#11239 After several experiments, and trained with a little corpus of 100,000words, the system guesses correctly not placing commas with a precision of 96% and a recall of 98%.
other,19-3-P06-2001,bq	It also gets a <term> precision </term> of 70 % and a <term> recall </term> of 49 % in the task of placing <term> commas </term> .	#11280 It also gets a precision of 70% and a recall of 49% in the task of placingcommas.
other,20-2-P06-2001,bq	After several experiments , and trained with a little <term> corpus </term> of 100,000 <term> words </term> , the system guesses correctly not placing <term> commas </term> with a <term> precision </term> of 96 % and a <term> recall </term> of 98 % .	#11247 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,25-1-P06-2001,bq	In this paper , we describe the research using <term> machine learning techniques </term> to build a <term> comma checker </term> to be integrated in a <term> grammar checker </term> for <term> Basque </term> .	#11225 In this paper, we describe the research using machine learning techniques to build a comma checker to be integrated in a grammar checker forBasque.
other,32-4-P06-2001,bq	Finally , we have shown that these results can be improved using a bigger and a more homogeneous <term> corpus </term> to train , that is , a bigger <term> corpus </term> written by one unique <term> author </term> .	#11314 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 uniqueauthor.
tech,15-1-P06-2001,bq	In this paper , we describe the research using <term> machine learning techniques </term> to build a <term> comma checker </term> to be integrated in a <term> grammar checker </term> for <term> Basque </term> .	#11215 In this paper, we describe the research using machine learning techniques to build acomma checker to be integrated in a grammar checker for Basque.
tech,22-1-P06-2001,bq	In this paper , we describe the research using <term> machine learning techniques </term> to build a <term> comma checker </term> to be integrated in a <term> grammar checker </term> for <term> Basque </term> .	#11222 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,bq	In this paper , we describe the research using <term> machine learning techniques </term> to build a <term> comma checker </term> to be integrated in a <term> grammar checker </term> for <term> Basque </term> .	#11209 In this paper, we describe the research usingmachine learning techniques to build a comma checker to be integrated in a grammar checker for Basque.


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