Structural Correspondence Learning for Dependency Parsing

Following (Blitzer et al., 2006), we present an application of structural correspondence learning to non-projective dependency parsing (McDonald et al., 2005). To induce the correspondences among dependency edges from different domains, we looked at every two tokens in a sentence and examined whether or not there is a preposition, a determiner or a helping verb between them. Three binary linear classifiers were trained to predict the existence of a preposition, etc, on unlabeled data and we used singular value decomposition to induce new features. During the training, the parser was trained with these additional features in addition to these described in (McDonald et al., 2005). We discriminatively trained our parser in an on-line fashion using a variant of the voted perceptron (Collins, 2002; Collins and Roark, 2004; Crammer and Singer, 2003).