Our method, thus, requires gold standard trees only on the source side of a bilingual corpus in the training phase, unlike the joint parsing model , which requires gold standard trees on the both sides.

Finally, we design the bilingual subtree features based on the mapping rules for the parsing model .

However, as described in Section 4.3.1, the generated subtrees are verified by looking up list ST): before they are used in the parsing models .

based parsing models (Nivre, 2003; Yamada and Matsumoto, 2003).

For dependency parsing, there are two main types of parsing models (Nivre and McDonald, 2008; Nivre and Kubler, 2006): transition-based (Nivre, 2003; Yamada and Matsumoto, 2003) and graph-based (McDonald et al., 2005; Carreras, 2007).

Our approach can be applied to both parsing models .

In this paper, we employ the graph-based MST parsing model proposed by McDonald and Pereira

Based on the mapping rules, we design a set of features for parsing models .

When segment length is not restricted, the inference procedure is the same as that used in parsing (Finkel and Manning, 2009c).3 In this work we do not enforce a length restriction, and directly utilize the fact that the model can be transformed into a parsing model .

Our parsing model is the discriminatively trained, conditional random field-based context-free grammar parser (CRF-CFG) of (Finkel et al., 2008).

In the parsing model , the grammar consists of only the rules observed in the training data.

We used the hybrid parsing model described in Clark and Curran (2007), and the Viterbi decoder to find the highest-scoring derivation.

For the biomedical parser evaluation we have used the parsing model and grammatical relation conversion script from Rimell and Clark (2009).

In the first two experiments, we explore performance on the newswire domain, which is the source of training data for the parsing model and the baseline supertagging model.