In this paper, we present a transition system for 2-planar dependency trees — trees that can be decomposed into at most two planar graphs — and show that it can be used to implement a classifier-based parser that runs in linear time and outperforms a state-of-the-art transition-based parser on four data sets from the CoNLL-X shared task .

In order to get a first estimate of the empirical accuracy that can be obtained with transition-based 2-planar parsing, we have evaluated the parser on four data sets from the CoNLL—X shared task (Buchholz and Marsi, 2006): Czech, Danish, German and Portuguese.

(2006b) in the original shared task , where the pseudo-projective version of MaltParser was one of the two top performing systems (Buchholz and Marsi, 2006).

Although the contributions of this paper are mainly theoretical, we also present an empirical evaluation of the 2-planar parser, showing that it outperforms the projective parser on four data sets from the CoNLL—X shared task (Buchholz and Marsi, 2006).

In recent semantic role labeling (SRL) competitions such as the shared tasks of CoNLL 2005 and CoNLL 2008, supervised SRL systems have been trained on newswire text, and then tested on both an in-domain test set (Wall Street Journal text) and an out-of-domain test set (fiction).

We test our open-domain semantic role labeling system using data from the CoNLL 2005 shared task (Carreras and Marquez, 2005).

Like the best systems from the CoNLL 2005 shared task (Punyakanok et al., 2008; Pradhan et al., 2005), they also use features from multiple parses to remain robust in the face of parser error.