Signal and Data Processing

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