This paper investigates the impact of different morphological and lexical information on data-driven dependency parsing of Persian, a morphologically rich language.We explore two state-of-the-art parsers, namely MSTParser and MaltParser, on the recently released Persian dependency treebank and establish some baselines for dependency parsing performance.Three sets of issues are addressed in our experiments: effects of using gold and automatically derived features, finding the best features for the parser, and a suitable way to alleviate the data sparsity problem.The final accuracy is 87.91% and 88.37% labeled attachment scores for Malt-Parser and MSTParser, respectively.