One-Step Statistical Parsing of Hybrid Dependency-Constituency Syntactic Representations

In this paper, we describe and compare two statistical parsing approaches for the hybrid dependency-constituency syntactic representation used in the Quranic Arabic Treebank (Dukes and Buckwalter, 2010). In our first approach, we apply a multi-step process in which we use a shift-reduce algorithm trained on a pure dependency preprocessed version of the treebank. After parsing, the dependency output is converted into the hybrid representation. This is compared to a novel one-step parser that is able to learn the hybrid representation without preprocessing. We define an extended labelled attachment score (ELAS) as our performance metric for hybrid parsing, and report 87.47% (F1 score) for the multi-step approach, and 89.03% (F1 score) for the onestep integrated algorithm. We also consider the effect of using different sets of morphological features for parsing the Quran, comparing our results to recent work on Modern Standard Arabic.