A Rule-Based Approach in Converting a Dependency Parse Tree into Phrase Structure Parse Tree for Persian

In this paper, an automatic method in converting a dependency parse tree into its equivalent phrase structure one is introduced for the Persian language. In the first step, a rule-based algorithm is designed. Then, Persian specific dependency-to-phrase structure conversion rules merge to the algorithm. Subsequently, the Persian dependency treebank with about 30,000 sentences is used as an input for the algorithm and an equivalent phrase structure treebank is extracted. Finally, the statistical Stanford parser is trained using the resulting treebank. Experimental results show a F1 of 96.05% for the conversion algorithm and an F1 of 86.01% for Persian factored model parser.