The M step then treats c as fixed , observed data and adjusts 0 until the predicted vector of total feature counts equals c , using Improved Iterative Scaling ( Della Pietra et al. , 1997 ; Chen and
inducing features of random fields
S Della Pietra V Della Pietra J Lafferty
â¢ If arc probabilities ( or even A , v , Âµ , Ï ) have loglinear parameterization , then the E step must compute c = Ei ecf ( xi , yi ) , where ec ( x , y ) denotes the expected vector of total feature counts along a random path in fÎ¸ whose ( input , output ) matches ( x , y ) . The M step then treats c as fixed , observed data and adjusts 0 until the predicted vector of total feature counts equals c , using Improved Iterative Scaling ( Della Pietra et al. , 1997 ; Chen and Rosenfeld , 1999 ) .12 For globally normalized , joint models , the predicted vector is ecf ( E * , A * ) .
The M step then treats c as fixed , observed data and adjusts 0 until the predicted vector of total feature counts equals c , using Improved Iterative Scaling ( Della @@CITATION ; Chen and