This choice is motivated by an observation we made previously ( Hasan and Ng , 2013a ) : since each post in a sequence is a reply to the preceding post , we could exploit their dependencies by determining their stance labels together .3 As our sequence learner , we employ a maximum entropy Markov model ( MEMM ) ( McCallum et al. , 2000 ) .
extralinguistic constraints on stance recognition in ideological debates
Kazi Saidul Hasan Vincent Ng
In other words , we train a SC model that assumes as input a post sequence and outputs a stance sequence , with one stance label for each post in the input post sequence . This choice is motivated by an observation we made previously ( Hasan and Ng , 2013a ) : since each post in a sequence is a reply to the preceding post , we could exploit their dependencies by determining their stance labels together .3 As our sequence learner , we employ a maximum entropy Markov model ( MEMM ) ( McCallum et al. , 2000 ) . Given an input post sequence PS = ( p1 , p2 , ... , pn ) , the MEMM finds the most probable stance sequence S = ( s1 , s2 , ... , sn ) by computing P ( S | PS ) , where :
This choice is motivated by an observation we made previously ( @@CITATIONa ) : since each post in a sequence is a reply to the preceding post , we could exploit their dependencies by determining their stance labels together .3 As our sequence learner , we employ a maximum entropy Markov model ( MEMM ) ( McCallum et al. , 2000 ) .