tions for the remaining 20 % of the instances ; and ( 3 ) train an SVM classifier ( using the LIBSVM package ( Chang and Lin , 2001 ) ) on these 20 % of the instances , where each instance , i , is represented by a set of 31 binary features .
libsvm a library for support vector machines software available at httpwwwcsientuedutw∼cjlinlibsvm
C-C Chang C-J Lin
7See http://www.cs.ualberta.ca/â¼lindek/downloads.htm 8In our implementation of Soon 's method , we label an instance as OTHERS if no NE or WN CLASS feature is generated ; otherwise its label is the value of the NE feature or the ACE SC that has the WN CLASS features as its keywords ( see Table 1 ) . tions for the remaining 20 % of the instances ; and ( 3 ) train an SVM classifier ( using the LIBSVM package ( Chang and Lin , 2001 ) ) on these 20 % of the instances , where each instance , i , is represented by a set of 31 binary features . More specifically , let Li = 141 , 42 , 43 , 44 , 451 be the set of predictions that we obtained for i in step ( 2 ) .
tions for the remaining 20 % of the instances ; and ( 3 ) train an SVM classifier ( using the LIBSVM package ( @@CITATION ) ) on these 20 % of the instances , where each instance , i , is represented by a set of 31 binary features .