We conducted experiments with gold features to assess the potential of these features , and with predicted features , obtained from training a simple maximum likelihood estimation classifier on this resource ( Alkuhlani and Habash 2012 ) .19 The first part of Table 8 shows that the RAT ( rationality ) feature is very relevant ( in gold ) , but suffers from low accuracy ( no gains in machine-predicted input ) .
identifying broken plurals irregular gender and rationality in arabic text
Sarah Alkuhlani Nizar Habash
To address this issue , we use a version of the PATB3 training and dev sets manually annotated with functional gender , number , and rationality ( Alkuhlani and Habash 2011 ) .18 This is the first resource providing all three features ( ElixirFm only provides functional number , and to some extent functional gender ) . We conducted experiments with gold features to assess the potential of these features , and with predicted features , obtained from training a simple maximum likelihood estimation classifier on this resource ( Alkuhlani and Habash 2012 ) .19 The first part of Table 8 shows that the RAT ( rationality ) feature is very relevant ( in gold ) , but suffers from low accuracy ( no gains in machine-predicted input ) . The next two parts show the advantages of functional gender and number ( denoted with a FN * prefix ) over their surface-based counterparts .
We conducted experiments with gold features to assess the potential of these features , and with predicted features , obtained from training a simple maximum likelihood estimation classifier on this resource ( @@CITATION ) .19 The first part of Table 8 shows that the RAT ( rationality ) feature is very relevant ( in gold ) , but suffers from low accuracy ( no gains in machine-predicted input ) .