The most successful supervised phrase-structure parsers are feature-rich discriminative parsers which heavily depend on an underlying PCFG (Charniak and Johnson, 2005; <cite>Huang, 2008)</cite> . These approaches consists of two stages. At the first stage they apply a PCFG to extract possible parses. The full set of possible parses cannot be iterated through in practice, and is usually pruned as a consequence. The n-best list parsers keep just the 50-100 best parses according to the PCFG. Other methods remove nodes and hyperedges whose posterior probability is under a predefined threshold from the forest (chart). The task of the second stage is to select the best parse from the set of possible parses (i.e. rerank this set).